{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting started with PyMC3\n",
    "\n",
    "Authors: John Salvatier, Thomas V. Wiecki, Christopher Fonnesbeck\n",
    "\n",
    "Note: This text is based on the [PeerJ CS publication on PyMC3](https://peerj.com/articles/cs-55/).\n",
    "\n",
    "## Abstract\n",
    "\n",
    "Probabilistic Programming allows for automatic Bayesian inference on user-defined probabilistic models. Recent advances in Markov chain Monte Carlo (MCMC) sampling allow inference on increasingly complex models. This class of MCMC, known as Hamiltonian Monte Carlo, requires gradient information which is often not readily available. PyMC3 is a new open source Probabilistic Programming framework written in Python that uses Theano to compute gradients via automatic differentiation as well as compile probabilistic programs on-the-fly to C for increased speed. Contrary to other Probabilistic Programming languages, PyMC3 allows model specification directly in Python code. The lack of a domain specific language allows for great flexibility and direct interaction with the model. This paper is a tutorial-style introduction to this software package.\n",
    "\n",
    "## Introduction\n",
    "\n",
    "Probabilistic programming (PP) allows flexible specification of Bayesian statistical models in code. PyMC3 is a new, open-source PP framework with an intuitive and readable, yet powerful, syntax that is close to the natural syntax statisticians use to describe models. It features next-generation Markov chain Monte Carlo (MCMC) sampling algorithms such as the No-U-Turn Sampler (NUTS; Hoffman, 2014), a self-tuning variant of Hamiltonian Monte Carlo (HMC; Duane, 1987). This class of samplers works well on high dimensional and complex posterior distributions and allows many complex models to be fit without specialized knowledge about fitting algorithms. HMC and NUTS take advantage of gradient information from the likelihood to achieve much faster convergence than traditional sampling methods, especially for larger models. NUTS also has several self-tuning strategies for adaptively setting the tunable parameters of Hamiltonian Monte Carlo, which means you usually don't need to have specialized knowledge about how the algorithms work. PyMC3, Stan (Stan Development Team, 2014), and the LaplacesDemon package for R are currently the only PP packages to offer HMC.\n",
    "\n",
    "Probabilistic programming in Python confers a number of advantages including multi-platform compatibility, an expressive yet clean and readable syntax, easy integration with other scientific libraries, and extensibility via C, C++, Fortran or Cython. These features make it relatively straightforward to write and use custom statistical distributions, samplers and transformation functions, as required by Bayesian analysis.\n",
    "\n",
    "While most of PyMC3's user-facing features are written in pure Python, it leverages Theano (Bergstra et al., 2010) to transparently transcode models to C and compile them to machine code, thereby boosting performance. Theano is a library that allows expressions to be defined using generalized vector data structures called *tensors*, which are tightly integrated with the popular NumPy `ndarray` data structure, and similarly allow for broadcasting and advanced indexing, just as NumPy arrays do. Theano also automatically optimizes the likelihood's computational graph for speed and provides simple GPU integration.\n",
    "\n",
    "Here, we present a primer on the use of PyMC3 for solving general Bayesian statistical inference and prediction problems. We will first see the basics of how to use PyMC3, motivated by a simple example: installation, data creation, model definition, model fitting and posterior analysis. Then we will cover two case studies and use them to show how to define and fit more sophisticated models. Finally we will show how to extend PyMC3 and discuss other useful features: the Generalized Linear Models subpackage, custom distributions, custom transformations and alternative storage backends."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Installation\n",
    "\n",
    "Running PyMC3 requires a working Python interpreter, either version 2.7 (or more recent) or 3.5 (or more recent); we recommend that new users install version 3.5. A complete Python installation for Mac OSX, Linux and Windows can most easily be obtained by downloading and installing the free [`Anaconda Python Distribution`](https://store.continuum.io/cshop/anaconda/) by ContinuumIO. \n",
    "\n",
    "`PyMC3` can be installed using `pip` (https://pip.pypa.io/en/latest/installing.html):\n",
    "\n",
    "```\n",
    "pip install pymc3\n",
    "```\n",
    "\n",
    "Or via conda:\n",
    "\n",
    "```\n",
    "conda install pymc3\n",
    "```\n",
    "\n",
    "The current development branch of PyMC3 can be installed from GitHub, also using pip:\n",
    "\n",
    "```\n",
    "pip install git+https://github.com/pymc-devs/pymc3\n",
    "```\n",
    "\n",
    "\n",
    "The source code for PyMC3 is hosted on GitHub at https://github.com/pymc-devs/pymc3 and is distributed under the liberal [Apache License 2.0](https://github.com/pymc-devs/pymc3/blob/master/LICENSE). On the GitHub site, users may also report bugs and other issues, as well as contribute documentation or code to the project, which we actively encourage."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A Motivating Example: Linear Regression\n",
    "\n",
    "To introduce model definition, fitting and posterior analysis, we first consider a simple Bayesian linear regression model with normal priors for the parameters. We are interested in predicting outcomes $Y$ as normally-distributed observations with an expected value $\\mu$ that is a linear function of two predictor variables, $X_1$ and $X_2$.\n",
    "\n",
    "$$\\begin{aligned} \n",
    "Y  &\\sim \\mathcal{N}(\\mu, \\sigma^2) \\\\\n",
    "\\mu &= \\alpha + \\beta_1 X_1 + \\beta_2 X_2\n",
    "\\end{aligned}$$\n",
    "\n",
    "where $\\alpha$ is the intercept, and $\\beta_i$ is the coefficient for covariate $X_i$, while $\\sigma$ represents the observation error. Since we are constructing a Bayesian model, we must assign a prior distribution to the unknown variables in the model. We choose zero-mean normal priors with variance of 100 for both regression coefficients, which corresponds to *weak* information regarding the true parameter values. We choose a half-normal distribution (normal distribution bounded at zero) as the prior for $\\sigma$.\n",
    "\n",
    "$$\\begin{aligned} \n",
    "\\alpha &\\sim \\mathcal{N}(0, 100) \\\\\n",
    "\\beta_i &\\sim \\mathcal{N}(0, 100) \\\\\n",
    "\\sigma &\\sim \\lvert\\mathcal{N}(0, 1){\\rvert}\n",
    "\\end{aligned}$$\n",
    "\n",
    "### Generating data\n",
    "\n",
    "We can simulate some artificial data from this model using only NumPy's `random` module, and then use PyMC3 to try to recover the corresponding parameters. We are intentionally generating the data to closely correspond the PyMC3 model structure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('seaborn-darkgrid')\n",
    "\n",
    "# Initialize random number generator\n",
    "np.random.seed(123)\n",
    "\n",
    "# True parameter values\n",
    "alpha, sigma = 1, 1\n",
    "beta = [1, 2.5]\n",
    "\n",
    "# Size of dataset\n",
    "size = 100\n",
    "\n",
    "# Predictor variable\n",
    "X1 = np.random.randn(size)\n",
    "X2 = np.random.randn(size) * 0.2\n",
    "\n",
    "# Simulate outcome variable\n",
    "Y = alpha + beta[0]*X1 + beta[1]*X2 + np.random.randn(size)*sigma"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is what the simulated data look like. We use the `pylab` module from the plotting library matplotlib. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline \n",
    "\n",
    "fig, axes = plt.subplots(1, 2, sharex=True, figsize=(10,4))\n",
    "axes[0].scatter(X1, Y)\n",
    "axes[1].scatter(X2, Y)\n",
    "axes[0].set_ylabel('Y'); axes[0].set_xlabel('X1'); axes[1].set_xlabel('X2');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Specification\n",
    "\n",
    "Specifying this model in PyMC3 is straightforward because the syntax is as close to the statistical notation. For the most part, each line of Python code corresponds to a line in the model notation above. \n",
    "\n",
    "First, we import PyMC. We use the convention of importing it as `pm`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running on PyMC3 v3.6\n"
     ]
    }
   ],
   "source": [
    "import pymc3 as pm\n",
    "print('Running on PyMC3 v{}'.format(pm.__version__))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we build our model, which we will present in full first, then explain each part line-by-line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "basic_model = pm.Model()\n",
    "\n",
    "with basic_model:\n",
    "    \n",
    "    # Priors for unknown model parameters\n",
    "    alpha = pm.Normal('alpha', mu=0, sigma=10)\n",
    "    beta = pm.Normal('beta', mu=0, sigma=10, shape=2)\n",
    "    sigma = pm.HalfNormal('sigma', sigma=1)\n",
    "    \n",
    "    # Expected value of outcome\n",
    "    mu = alpha + beta[0]*X1 + beta[1]*X2\n",
    "    \n",
    "    # Likelihood (sampling distribution) of observations\n",
    "    Y_obs = pm.Normal('Y_obs', mu=mu, sigma=sigma, observed=Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first line,\n",
    "\n",
    "```python\n",
    "basic_model = Model()\n",
    "```\n",
    "\n",
    "creates a new `Model` object which is a container for the model random variables.\n",
    "\n",
    "Following instantiation of the model, the subsequent specification of the model components is performed inside a  `with` statement:\n",
    "\n",
    "```python\n",
    "with basic_model:\n",
    "```\n",
    "This creates a *context manager*, with our `basic_model` as the context, that includes all statements until the indented block ends. This means all PyMC3 objects introduced in the indented code block below the `with` statement are added to the model behind the scenes. Absent this context manager idiom, we would be forced to manually associate each of the variables with `basic_model` right after we create them. If you try to create a new random variable without a `with model:` statement, it will raise an error since there is no obvious model for the variable to be added to.\n",
    "\n",
    "The first three statements in the context manager:\n",
    "\n",
    "```python\n",
    "alpha = Normal('alpha', mu=0, sigma=10)\n",
    "beta = Normal('beta', mu=0, sigma=10, shape=2)\n",
    "sigma = HalfNormal('sigma', sigma=1)\n",
    "```\n",
    "create **stochastic** random variables with Normal prior distributions for the regression coefficients with a mean of 0 and standard deviation of 10, and a half-normal distribution for the standard deviation of the observations, $\\sigma$. These are stochastic because their values are partly determined by its parents in the dependency graph of random variables, which for priors are simple constants, and partly random (or stochastic). \n",
    "\n",
    "We call the `Normal` constructor to create a random variable to use as a normal prior. The first argument is always the *name* of the random variable, which should almost always match the name of the Python variable being assigned to, since it is sometimes used to retrieve the variable from the model for summarizing output. The remaining required arguments for a stochastic object are the parameters, in this case `mu`, the mean, and `sd`, the standard deviation, which we assign hyperparameter values for the model. In general, a distribution's parameters are values that determine the location, shape or scale of the random variable, depending on the parameterization of the distribution. Most commonly used distributions, such as `Beta`, `Exponential`, `Categorical`, `Gamma`, `Binomial` and many others, are available in PyMC3.\n",
    "\n",
    "The `beta` variable has an additional `shape` argument to denote it as a vector-valued parameter of size 2. The `shape` argument is available for all distributions and specifies the length or shape of the random variable, but is optional for scalar variables, since it defaults to a value of one. It can be an integer, to specify an array, or a tuple, to specify a multidimensional array (*e.g.* `shape=(5,7)` makes random variable that takes on 5 by 7 matrix values). \n",
    "\n",
    "Detailed notes about distributions, sampling methods and other PyMC3 functions are available in the [API documentation](https://docs.pymc.io/api.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Having defined the priors, the next statement creates the expected value `mu` of the outcomes, specifying the linear relationship:\n",
    "\n",
    "```python\n",
    "mu = alpha + beta[0]*X1 + beta[1]*X2\n",
    "```\n",
    "This creates a **deterministic** random variable, which implies that its value is *completely* determined by its parents' values. That is, there is no uncertainty beyond that which is inherent in the parents' values. Here, `mu` is just the sum of the intercept `alpha` and the two products of the coefficients in `beta` and the predictor variables, whatever their values may be. \n",
    "\n",
    "PyMC3 random variables and data can be arbitrarily added, subtracted, divided, multiplied together and indexed-into to create new random variables. This allows for great model expressivity. Many common mathematical functions like `sum`, `sin`, `exp` and linear algebra functions like `dot` (for inner product) and `inv` (for inverse) are also provided. \n",
    "\n",
    "The final line of the model, defines `Y_obs`, the sampling distribution of the outcomes in the dataset.\n",
    "\n",
    "```python\n",
    "Y_obs = Normal('Y_obs', mu=mu, sigma=sigma, observed=Y)\n",
    "```\n",
    "\n",
    "This is a special case of a stochastic variable that we call an **observed stochastic**, and represents the data likelihood of the model. It is identical to a standard stochastic, except that its `observed` argument, which passes the data to the variable, indicates that the values for this variable were observed, and should not be changed by any fitting algorithm applied to the model. The data can be passed in the form of either a `numpy.ndarray` or `pandas.DataFrame` object.\n",
    "\n",
    "Notice that, unlike for the priors of the model, the parameters for the normal distribution of `Y_obs` are not fixed values, but rather are the deterministic object `mu` and the stochastic `sigma`. This creates parent-child relationships between the likelihood and these two variables."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model fitting\n",
    "\n",
    "Having completely specified our model, the next step is to obtain posterior estimates for the unknown variables in the model. Ideally, we could calculate the posterior estimates analytically, but for most non-trivial models, this is not feasible. We will consider two approaches, whose appropriateness depends on the structure of the model and the goals of the analysis: finding the *maximum a posteriori* (MAP) point using optimization methods, and computing summaries based on samples drawn from the posterior distribution using Markov Chain Monte Carlo (MCMC) sampling methods.\n",
    "\n",
    "#### Maximum a posteriori methods\n",
    "\n",
    "The **maximum a posteriori (MAP)** estimate for a model, is the mode of the posterior distribution and is generally found using numerical optimization methods. This is often fast and easy to do, but only gives a point estimate for the parameters and can be biased if the mode isn't representative of the distribution. PyMC3 provides this functionality with the `find_MAP` function.\n",
    "\n",
    "Below we find the MAP for our original model. The MAP is returned as a parameter **point**, which is always represented by a Python dictionary of variable names to NumPy arrays of parameter values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/twiecki/working/projects/pymc/pymc3/tuning/starting.py:61: UserWarning: find_MAP should not be used to initialize the NUTS sampler, simply call pymc3.sample() and it will automatically initialize NUTS in a better way.\n",
      "  warnings.warn('find_MAP should not be used to initialize the NUTS sampler, simply call pymc3.sample() and it will automatically initialize NUTS in a better way.')\n",
      "logp = -149.58, ||grad|| = 12.242: 100%|██████████| 19/19 [00:00<00:00, 1478.46it/s]  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'alpha': array(0.90660093),\n",
       " 'beta': array([0.94848596, 2.60711845]),\n",
       " 'sigma_log__': array(-0.03771373),\n",
       " 'sigma': array(0.96298858)}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "map_estimate = pm.find_MAP(model=basic_model)\n",
    "    \n",
    "map_estimate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default, `find_MAP` uses the Broyden–Fletcher–Goldfarb–Shanno (BFGS) optimization algorithm to find the maximum of the log-posterior but also allows selection of other optimization algorithms from the `scipy.optimize` module. For example, below we use Powell's method to find the MAP."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/twiecki/working/projects/pymc/pymc3/tuning/starting.py:61: UserWarning: find_MAP should not be used to initialize the NUTS sampler, simply call pymc3.sample() and it will automatically initialize NUTS in a better way.\n",
      "  warnings.warn('find_MAP should not be used to initialize the NUTS sampler, simply call pymc3.sample() and it will automatically initialize NUTS in a better way.')\n",
      "  0%|          | 0/5000 [00:00<?, ?it/s]/Users/twiecki/anaconda3/lib/python3.6/site-packages/scipy/optimize/_minimize.py:502: RuntimeWarning: Method powell does not use gradient information (jac).\n",
      "  RuntimeWarning)\n",
      "logp = -149.47, ||grad|| = 13.248: 100%|██████████| 177/177 [00:00<00:00, 1276.05it/s] \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'alpha': array(0.90907964),\n",
       " 'beta': array([0.9514399 , 2.61452795]),\n",
       " 'sigma_log__': array(-0.03492212),\n",
       " 'sigma': array(0.96568062)}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "map_estimate = pm.find_MAP(model=basic_model, method='powell')\n",
    "    \n",
    "map_estimate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is important to note that the MAP estimate is not always reasonable, especially if the mode is at an extreme. This can be a subtle issue; with high dimensional posteriors, one can have areas of extremely high density but low total probability because the volume is very small. This will often occur in hierarchical models with the variance parameter for the random effect. If the individual group means are all the same, the posterior will have near infinite density if the scale parameter for the group means is almost zero, even though the probability of such a small scale parameter will be small since the group means must be extremely close together. \n",
    "\n",
    "Most techniques for finding the MAP estimate also only find a *local* optimum (which is often good enough), but can fail badly for multimodal posteriors if the different modes are meaningfully different.\n",
    "\n",
    "In summary, while PyMC3 provides the function `find_MAP()`, at this point mostly for historical reasons, this function is of little use in most scenarios. If you want a point estimate you should get it from the posterior. In the next section we will see how to get a posterior using sampling methods."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Sampling methods\n",
    "\n",
    "Though finding the MAP is a fast and easy way of obtaining estimates of the unknown model parameters, it is limited because there is no associated estimate of uncertainty produced with the MAP estimates. Instead, a simulation-based approach such as Markov chain Monte Carlo (MCMC) can be used to obtain a Markov chain of values that, given the satisfaction of certain conditions, are indistinguishable from samples from the _true_ posterior distribution. \n",
    "\n",
    "To conduct MCMC sampling to generate posterior samples in PyMC3, we specify a **step method** object that corresponds to a particular MCMC algorithm, such as Metropolis, Slice sampling, or the No-U-Turn Sampler (NUTS). PyMC3's `step_methods` submodule contains the following samplers: `NUTS`, `Metropolis`, `Slice`, `HamiltonianMC`, and `BinaryMetropolis`. These step methods can be assigned manually, or assigned automatically by PyMC3. Auto-assignment is based on the attributes of each variable in the model. In general:\n",
    "\n",
    "* Binary variables will be assigned to `BinaryMetropolis`\n",
    "* Discrete variables will be assigned to `Metropolis`\n",
    "* Continuous variables will be assigned to `NUTS`\n",
    "\n",
    "Auto-assignment can be overriden for any subset of variables by specifying them manually prior to sampling."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Gradient-based sampling methods\n",
    "\n",
    "PyMC3 has the standard sampling algorithms like adaptive Metropolis-Hastings and adaptive slice sampling, but PyMC3's most capable step method is the No-U-Turn Sampler. NUTS is especially useful on models that have many continuous parameters, a situation where other MCMC algorithms work very slowly. It takes advantage of information about where regions of higher probability are, based on the gradient of the log posterior-density. This helps it achieve dramatically faster convergence on large problems than traditional sampling methods achieve. PyMC3 relies on Theano to analytically compute model gradients via automatic differentiation of the posterior density. NUTS also has several self-tuning strategies for adaptively setting the tunable parameters of Hamiltonian Monte Carlo. For random variables that are undifferentiable (namely, discrete variables) NUTS cannot be used, but it may still be used on the differentiable variables in a model that contains undifferentiable variables. \n",
    "\n",
    "NUTS requires a scaling matrix parameter, which is analogous to the variance parameter for the jump proposal distribution in Metropolis-Hastings, although NUTS uses it somewhat differently. The matrix gives the rough shape of the distribution so that NUTS does not make jumps that are too large in some directions and too small in other directions. It is important to set this scaling parameter to a reasonable value to facilitate efficient sampling. This is especially true for models that have many unobserved stochastic random variables or models with highly non-normal posterior distributions. Poor scaling parameters will slow down NUTS significantly, sometimes almost stopping it completely. A reasonable starting point for sampling can also be important for efficient sampling, but not as often.\n",
    "\n",
    "`PyMC3` automatically initializes NUTS to reasonable values based on the variance of the samples obtained during a tuning phase. A little bit of noise is added to ensure different, parallel, chains start from different points. Also, `PyMC3` will automatically assign an appropriate sampler if we don't supply it via the `step` keyword argument (see below for an example of how to explicitly assign step methods)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (2 chains in 2 jobs)\n",
      "NUTS: [sigma, beta, alpha]\n",
      "Sampling 2 chains: 100%|██████████| 2000/2000 [00:01<00:00, 1479.97draws/s]\n",
      "/Users/twiecki/anaconda3/lib/python3.6/site-packages/mkl_fft/_numpy_fft.py:1044: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  output = mkl_fft.rfftn_numpy(a, s, axes)\n"
     ]
    }
   ],
   "source": [
    "with basic_model:\n",
    "    # draw 500 posterior samples\n",
    "    trace = pm.sample(500) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `sample` function runs the step method(s) assigned (or passed) to it for the given number of iterations and returns a `Trace` object containing the samples collected, in the order they were collected. The `trace` object can be queried in a similar way to a `dict` containing a map from variable names to `numpy.array`s. The first dimension of the array is the sampling index and the later dimensions match the shape of the variable. We can see the last 5 values for the `alpha` variable as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.86038143, 0.88875012, 0.91392392, 0.99143432, 0.86691189])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trace['alpha'][-5:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we wanted to use the slice sampling algorithm to `sigma` instead of NUTS (which was assigned automatically), we could have specified this as the `step` argument for `sample`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (2 chains in 2 jobs)\n",
      "CompoundStep\n",
      ">Slice: [sigma]\n",
      ">Slice: [beta]\n",
      ">Slice: [alpha]\n",
      "Sampling 2 chains: 100%|██████████| 11000/11000 [00:09<00:00, 1156.62draws/s]\n",
      "/Users/twiecki/anaconda3/lib/python3.6/site-packages/mkl_fft/_numpy_fft.py:1044: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  output = mkl_fft.rfftn_numpy(a, s, axes)\n"
     ]
    }
   ],
   "source": [
    "with basic_model:\n",
    "\n",
    "    # instantiate sampler\n",
    "    step = pm.Slice() \n",
    "    \n",
    "    # draw 5000 posterior samples\n",
    "    trace = pm.sample(5000, step=step)   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Posterior analysis\n",
    "`PyMC3` provides plotting and summarization functions for inspecting the sampling output. A simple posterior plot can be created using `traceplot`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qhfd5AREGmuCowjjyIvV/uAzDxG7+u/WLvH/k7Vxz2jo+ed665TVRpSTR/Mab2HPcTZyTeo6x31xH/2SNqmMJBEcK/h5Moy9h8O3HOPYytslXq9ptIphg90igQAGRQmPIVYwkZ8qxJ4q9JdLcPs/BWBJ/eAnkFc0D0YQWSlRMfihaSlF5dXiGiQoGHVDR6DIPPo259yGkyCTmgacqKqeHghT1aVMwlCGWVHiye4rB6RgECj0ajYE9dEw9N+cw0GqwHLh3TsZfJeTgMOa+x5D9B3lo7wTP95b3gB4W6tzDjPt9EfZPhBZAGA0pPIG571EM/u7SdSnNCJbDhQaNZf+fMB/4S0k7cmCoYJk1MowpEcB98C7kYOG6YhIpdR5yI7XnJN8YHgvEeK4nV4FxPBDHkIogh0bKNJF51nLPaWbJoD/KXu9OzctbTTVNde5GbDSR4r49YyV5qBmHdc/UoYUmZlVDFVZMPcOasQcPqZ25Igw0wVGDZe/t1N9+Faps5stN3+Ff+jZxw1ld3HD22uVlnOXR/PqPsv/4mzhffY6x313P/lFhpAkEh4o0qhlkUiqOwX8Qy0xlrwRASlEYno6iIhFJz6uEqmIefJp2X/XTYmQ8cPJMX0muUDVeiacOTPFc7zwo3RlvQGgMqgxVqqZk+4BfU5pmorMbkc/3+dkxoBNOJIGKRDSZYjIcR1EoGBV3hQ5iTmjvwJ7JMC/ltaGWyTOSlCRyTNsu42U6VAb8Efr9OSXQ3PdYQRinLxwvKA6SVLT+oud9rQtrIaeZkMSJUG7f+/aMsb3/8AwhU6p6w8UfSfDUgSmSOvdYSmgh9nLakMx4JmT/QYzDz2PuqV6ZrdTPTf2PV91Oht0jAbrHC8/TOP6KvrEXD5Xt78bxV+kauRdHpNBQkhJa21KscuibFJsuCNMs9sCYB57ENPzXAmO8fnIHnZNPAWnv6GFMESCnYtlzno4k2DdemsdpSoUJFD2bO4dm8EcSBc925+RTmAafrXzAMvqUK9ynrT7EEEg5MIRl3526HsjRQIzH0l7Ewem0gVacE6gqGMde1r/PyQjG0R30TYXKzounomJOLF60kjDQBEc+qortxR/ieuDvibedyN85/o1f9rr45Hnr+ODpq2st3WHjPvuj9B3/j1zCU4zdegOvDC7QCKZAcKRTRgkamYmhqioHJ8OaspIIa+FAvu6CSmtq0V/llOBKw0GmkRcxjWzX5m1CU8wfeG28QGmIJxUCvlFNaT0Ez8KsqArmwacxDTyNcXRHxdwmgJcGZ7jn1dKclnyZfWnFPTM5cAYpMlmgSJkTfqJRTcEqzquR0v/1hRKMTGtKVkpRs4ZDY8DLikktVNA7FmQ0EKOaQNXifBx5uueQcnR2DQeyc06F4ilU1AJl/K+9/oLiIHJakU1U8H5IsWn2jAbY3le470SweoXdF47zfJ+f2CFW/t07FiQYSzJdbVhYKoZp7GUMgUGUWID79ozNmseZSCk88No49+0ZYyIY03IF85Cj2nft2Z4p7n9NP8xODgxqCngFDL5u5MBgiQ1h6XkA88EHdPeZ6NeMq5bpncgzAzoGWa6xHQPTPN9XOFCi9DzOQe+LJOKVDZPinMwMcng8O0WANPgc6gH97crNd7Z6/BFMg1oxFu9YkEhcKclt65x4koOTs3uY5IrhfTrPmqJQF+5FyveIHWIYrGn4r6CqSMnSvPvh6UKvmRwYxHLwPqTQWNYYdUSHMfgPYiwqEBSKJ4n0v4gydZCh7p3syOSaFXm/FzsCWhhogiMbJYXjyS/hfOZfCK9/Mx/mc9zbk+SmCzfwnpNW1lq6ecN29scZ2fZx3spjTNz2cV46zNFVgeDoJvcldkS0fKd+f5S9Y0EOToU1RQFtNH46klZYJAklbSikosGcJyEVwzic86Qlkin2543qZzxL+USTKYz92pxpmWIB+YbN831+el59CjkyNa8hcCoqTx+cYuagJq8cn8Ew3TvrfnrhiFPhOI/tnywJNyrG3P8EloP3pQVQWTH5LK3+7brbShIlo/OhWIqHiubz2jU8t0iCzIi+HPNDIkK07wUMeV6CsUCs7Kh6Ps5wH4Z0aNtEMM5w2ohESeIPlSq/+WcSjSfLFnoYmJjBFjv0io7D6Qp4gZhmNKaKjmPwH8A4ol3zAX+kxLviS4fO9kwVKsbP9/nYoTcgmLd/LKHQOfE4IyO5vEHvWLAkNDURzfXj/RMhHt47UdJ3RgMxpiPJsoqyafj5WXPx8rFHR7HEc0avpCQxjr+SvRag5SaO5VUQNI28QPi1+7lvz5huGfyxQKxkCoPRmRiRuMJ4UDufREohpWhe57mGJff37mdvr34O5hPd5fPQ5HC6/8w2oFPm4vbMaX6+XM+2R4dpmtmDO9iNMVW+jcw1qQpVGyQrOGKRwZ0xohPByVzeafbcC4/zZPcUe0YCKCo0BPdp4eaJMJYD92GYfC3b9mJnqAoDTXDkkoziuv8j2Hf+jOC267kh8hEePRjkpgs3cOXxK2ot3bxjOPtGJo67gXfIDzFyx43sEEaaQDAnxmaiJRMFt0y/gi02jhoPY0hFNSUiT4k5kJfj4h0Lgqoy9vLd9E1FUJFoCO0jON6T3cb34u9pns6VoR4v8oSkFJW9oyH6fRGiyRTReFqpyFNAEoFxzMlMOJ6+P66aqm7+SKIg5DCpQCCaYLjXm2lE+wfNe1gc4qaqKq8M6RtDM+mwvbkk41sSvvS/pSFjwViS3qnqquJljF5DKkoykpavUhh7WnGTEmGie+7m4ESYV4dyBoPmFSl9n77Q52coM3KfjNI8s5u2POMy49EIvHwbI8/fWlHm3SMB/fmVVJUW/w7afNuRlcK+IsWDSLHqjdF4UmHPSJBdQ4XHMY7txDDTz2ggxq7hAPtGfOwdmsp6sVZMPEnTzKslZcmnQglC0VIjRcpTnlXAlAzTGNiT9ZTGEgpDeQMT5v13Y+99EFM6fCwY09qcTBsvoXgK71iwqvDYudDq30HH1F8Llhl83RjSIca+cJy+0VKjZzotRyhWrTzpIhPpvrtnJMizvT4e9I7zXK+vxFtYtpXIFKFY7npL4QnU+Nxy7Op9O2ffCCCVQFLS5ydJeMeCyErp+U6F4zljVefRzOwjq0mk9Pmb+x7FOPxCdptESuuXezPhyrOck8G3D8vB+yv3/XQObzR+aKGhmXnzDMEh3WkY4kklO3i2UAgDTXBEIsWmcd/1Xizdf2bmjJv5u6mreOyAj09fcGQaZwBIEuo5n2Nyy//jffK9HLzj87w4IIw0gaBaBvwRAtFkyZw7kprE1nM/q8YfLflUSxQqV6b+x4lnFK60UdCbFzrkCyVwRkqT/htndiOFJ7KtBeMpIvFc2/nHdUTLh9/J/oPIE7u5/7VxvDoVE42jL2X/fq7HxzMHfSUhWcVMR5L0DvSzf7ywvUhCyRkoZVDR5rtqnZh9vqtiZTmfv/b5SSkqdZG+WdvJsGr8UYb2lJ9bSY94stQIhcLwsalQDNPAUwR9o7wyNMOBiUBWoSswoiTNuB3yR5FUddbqb5lrWR/IFWpIKSrmlHbdpSLvh7r/Psy9Dxc2kkqUVADN2KYTwXi2dL9eTl7G86jsvRfVezd7057eJlOUuvAAK9xWXbnzPXKOyBCvPXd3Lg8orbVLkPOUFpG5Lqa0h6XYkzI8HSWWUAjEctdvNo/ObAMUBQMjZWz353smCfcU9sloMpUt6KN3BGe4fP909ubOPxSOYEoEkNQUoXiKnYMzhOOpgnPM0D0RYufgDOaiPDzzwJOMv3SX7rF6p8JZzyfkrqkl7YlNJCtfH0v3n1k5Upg/6AqXeicHJ/yYE9P4wgkSilbF0xcp9QYXvCdVBUNgAOPwC0ztf5qHvTmZ5MAQlp7SUNPVo/dn/854A/NDHYvrCCRSKiOBGKTirBx/DEdkEFN8huloAkNgsGSwo4Ss2yxXgCWS9w54ZJ9WEGchjTTjgrUsENQIKTKF+673YJzy4rvw+/yDdyOP7p/gxvPXc/UJR6hxlkGSUM77Mr5kmA/v/S3/fpsN9W1f4KRV9bWWTCBYNozMROh0FC7zlQlFcocySov2QZejvjx9b/biQxk9wBXuY+zVYVqc5orbzYZp7OV0ZchmeqbCtNVZMBklHGbtc2+Y7inZZyqUAAMF3qlIIoXNpM0JqagqHb7nwP16TP0vodibSTVtrk4gtHat8UKvWEJRkaW8cucVqh2CVsIdwKiT4O+IDCFF9NWZXHVM/QuYSCnEYkmcOqsD0SSNg/fjCjUy41intaIkeLm7l0us43RMdZMwOpjxWTE3J3Tlm8krALJm7EF62i7O3kwpGSnIzTEkQshqgvrQgZx8FfLTvKMh1rc4ChQ50/BfkcPjxDZcDnIVKp6SIhhP4o8kSTgUkKRcnlG6P5jS089kp6FJhNk3lVNMdw0F2Nqg/W1JToMKk2kDqM5SOK/oXBTaSl3eOxZkZb0Vo0EmNLqfhgHNqFVRtVDMzG1XFaRA6YBIsREYiCZIqVBvM+WOr1NJcO9ozruTK1qYk7R5ZjdBe2Fuu5pePzIdy86z2u57Hku6oE3GpMuEPnvaHAQiuX6T7zUrxhdKQJ32tzPchxQ1oVobeK1ocKbY+93vi7BRVasukGZKBpHT10NFRYpMIiXCNI09jzkQhDWrePrAJK3+ACGrA7c7zPrm3Es0U/RGRSUUT+E0GzEEBhgfnIG2ddnt5Ij+3GRyweBEqXGZn4M2GYrTPTODMhOjWerFmIrQMq0V2+mPgttqosX/EqONp+a1IGUr6qoqmHsfya6pVHyp2smuDwVhoAmOKKTwOPV3vgvDdA++S3/Kp19p5+F9E3zyvHW888TOWou3OEgSyQu+wXQyxKcO/I6v3G5FveJGTl4tjDSBoBoi8RQ49Nfl50GNBKLZsB09ylUOLMdUKFEwga6ap4johdlEEiny1V9L3Ae2wijA53p9SEqCN7kOkmw7HtBCxoKxJNhz2x2cCrPSbUVKH3PfWIhtna6C4zmm9yLbwgyPDPLauIMT1+XeqSsH70G1HAetxyJJEpZAH45IFFifzWmyR0dQJRloZc9wAItJxtPqBMA0uiPvvCXcwf2YvE8R91zB2EyENSP3ErR26F63lumdmPt7dNdVyhwJxpMcGA+TMKqc1GYoWf/0wSnOjgVpDExkDbSVE49jUBL02jT1yZQMYUqGUDNacvHRKzgqbD330+bLGQQrJ54ov3Gal4qqW3aPh/Dk/U6EJrFIYJjpI+VeS1KFvqkIbTptWeJT8NoTHBhPeyLK9PliJl+6CyUYh/ZLssviOmF6k8E4kUT6uqoqIDHgq34ONnN4BF84Ubb4xSvDAdY32+nbuwO5QabebGAymMCbCmI0SEhKgobgPkLd45jtpoJ9i4tWZApk1Hemt1MVrHn5aXoYioqFZKogtk89i9mgr7hnvOIZ40wP75h+iF+5ybVdoYMYlDju0EHMfQPEV5+XXdc+9Vz6r7QnM69DplIKUb3J0IuqJKZSCp0ThZ5oczpH1pAekHh1eAZnXghkNG24FPf/8WCCkekoa5ts1Fm1a90YeC23QTo0UVHVbBGdShh83aAkgMbsMlUF+0w3Qco//UYlijnhJzgWBLR3ULl+VomZyMLNiSZCHAVHDHJwmPrbr8Iw04fvTf/L53ev4MG9E3z83HVHVEGQqpANxN/4nwRWXcTNhl/w6B0/OOyyzALB0YJUQamXkBidiTIwHWVsptQbsGc0mFXCJNSKGnomdMaUzOUExfKKgYTz/tbTVfaNhQpCidrSZf2Lj2iLTyFHJjBOaNXLusdDjBaFcQYiyZLiEWpRS6aolo8zGYzTMvxoyfq9u3dw/2ta+JFzcict0zsJx1PZfK5W/0u0+V4sOVd/JMGrRcUmGoL72T8Z4sCoj1cGfKCCM1JdZcXOiSdYNZ4L/fOFE7w2VhrumTVMytDifzE3dUIaQ1oJnS5SzPQ8rHrq5arxh2mc2Y0pGSCaVLDOOh9Z7hqvGn+U5MALZbcMxVN4h/xMhuJaXplvH+PpufmKQ7oc0RGs8amKHoCm4UchlSt6MRGM0zMZLqggaY1rfWJ7OkeveFCimlxIbb+cpym9I2r/M/T7NMNJkY0FCrSsJEi5UVbhAAAgAElEQVTNjGTlz1QlVNLXK5VSWDP2EK5wH/2+CIFYUptQPpYkkVK0nLg0lnBp0Q3Lvrto9b9UsjyDLTZGaGxfwbKMkWeN+9k5OEM0XTXTMNey8mUuWX6uWn5l1caAN8+Tr+V4ZcjvX6bBp5HUXL819zzAUwd0jFC1sG+/VKEydHaKMBWaZrRql5aEnwFfJF1ts/BkMpVEE3kevfyQbVU2anMcDgUYCcRK+k8kkSKa6QeqyuSBF9i/u/CZqBRmqii5Y6+YfJZIby6EdTRd7ChTECiRUpiermykFxfOmU+EB01wRCCHRnHf8Q7k8Dj+N/+ar+5q5C97hrnhrC6uOfkoM84yGExEL/sv5D+9n38Z+jGfut2C6aoPsW2Fa/Z9BYKjmLpwPzToPyeusecYH8+V+S425vJD0iqXpAab38vKCa9uGFsqpTKVCRML92EZfA26zgK50NOj+SUywuiXG1MlibFgHKspSc9g3sh9WvnJVFdLpFSQczt3j4fZ0OIo8N7lF/3Iln3PCz+qD+4jEM2NZgdjyfT4dN65FYdb+SOg5EIcM+FMsbhCMpGsmC+iF4ZmSuY8EImkqhVcMVuq9hBlcETHUNMhp7KSwKCUV7TzPUOykkCRTbrbGZQErnAfrnAf1dcbzFEXGURCIW50Z5eZBp5iaDqarSoYjqdocqTn6TJqYZmWovmb7LFx7LFxogZL2WMpsSByKNfXRwMxRgMxuoraqUTmVms5SOV9AhnbwxqfpC7cS8xUj1IQ1aYQiqVont6JioQjNorNoCA3OzElIwSiRhJWI3kmQ0H7ByfCtLosWkXGiccwmRuy68wjL5K5szsHZ1jfYs+GBJfDlAwTiqZIKSpSUt8ruHc0xDZj4fPqj8xPzpKKqutVr0T+vQQgGcEZ7seVKJRfSoSzxoqkJPGHFfLjb1QVdo3MYDLI2E2lnufMtddeL4XvNl+odCDDkOd5e6Q/xamy9t4cm4kxkVeEKRRP0j0eJiXHeV2HDf/ga9mBpvrAXmRSTDk30TyzG9WqyaV3hfYMB8CohQ9MRxLg1PI7i/Py9oykcz/bUqAqqGWe6YVCGGiCZY8UmcR957sxhEbxvfn/+PbeRm7bOch1p646IuY5OyyMVsKX/xzpjnfz7bHv87HbrJivfj+b2vTDcQSC5YTH47EA24FPeL1e3RlxPR7PGuB/gLPQ0j0+5fV6/zJb2xOheC7nJg8lMKK7fV24n7ClpXrhAefMfqqZGrlpZjcHZmBbcorYxisK1ln6HkZxdkKB2gyGVISUwZb9PTIdJRILYKMQV/ggjemiFAP+CG1yYRXCREopmFx5NFhqpHSN3g9WTZ2oD3ajqKdWDM950DteIG0lJ4va8wQdavlqeZ0TWuGEnYOzVTOsTpkdLjMtQMfUM5h05l/So3l6J2MNJxGKpQjFZp9bqhKKTll0zZOY8zq8uq+7cANJ8zT4A1GwaRX0ypGZT04PvQm0Z6P4WJl7q1278oOD3eMhaID2Kc0LXGz4yapCq297wfKYAuzVcoVUtPnaWuusdI3cS9BWmm8ezxsIyfcsFZ+nL5KsuuT7wckwG+TRqrYFCgr/zJW+qcK+pDdxeMar3TVyLzGTK2+5DqpK88wuzFYj+WajafAZptIe4RVTTzHUdHbJrooCMUXJ5tSVJVm50mWd1Vhy/fOLKoX8Y2RCELvTHm+DkkDFyvBQf3a7TN5m1KQNDlXK28snlQ7xzO9XlriP8bw5MeuD+3GHDtLXcn5Vbc4XIsRRsKyRon7cd70Hw0wv05f/gv/qaeE32wd55+tW8JGzu2ot3tLAZCf8ll+RaPTwHenf+fmtt7B/Ym6leQWCpYbH47ECvwW2VthGAu4EJoFTgP8F/ujxeNbO1n7+HEwNwf3Zv0tybfIU6PzwvcVCVbVS0AXLoKDQRAZToriMu4olkQtfSiTVkpC7PSPBbIhbcUhjOeTIRLX20KyYkiEMZSYQB/2iIfqo2XsVTepXy8tsVvAzUyijSuMMwKBUn2c1G/t0QjOr2y/EgYkw3XN615fe34oTTOf1fVe4j47JZ7LFIHIt5toc8Fc2VnNTR6Cb26nnrcsPCU4puW6nVyl1LsxW+d4V1uYHDMdTKKqazT9bTHYPl96bVwYD7E3nsFXKcwN9Ay+lqNmKpACmZKQkSqA47LcSlmTplBn5GHQGwfJpnngG4xyevWwfVgt/VsJd9K7smHquoOhIJnzUmveu7Bq5l47Jp7DG9IuazAfCQBMsW6R4APefrsE4tY/py37Kz4ZW8T/P9PGWY9v41Pnrq65OdDSgWlyEr/gNqmsV3+Mb/PD3f6R3AWOnBYKFxOPxbAGeBdbPsun5gAf4kNfr3e31er8BPA38v2qOkxv9L6/kyrNN/Dr7UareMqGoWkjOLHurgDMyUGKQGVPzZzgAOMP9usuTQzuyJePtOvk9tcAa92uePlWbZ+7gRO79Z0qG8EcSugZolSlUC0alfMhqqNaTUI7xQGGfMeT1oa680uegP39dfthY8QTO2eXpyo4NedMLHCr+RTKUjKmcsXlgIpzNPysmPxftUEjoFfGgcr+M6RhQevMR7tMpRnJwMpyd5y3vaAW/usdL97PkDezkD5rYYvqTZ5crFmNKlg5ImFKly6qd01oq4z3OH3CZrd9lciNb/TsKllsSAdp95XNCDxdhoAmWJ4kwrruvwzjxKjOX/IjfTG3ku48f5CJPC5+7aGNV1X+ONlRbE+Erb8HgaOH76tf4zu/vZHD68MJvBIIacQ5wP3DGLNudDuzwer35lsqTVeyHopAtUJDPXPM+6iLlDRRfODHrfET57BkO8Hz3MK68JPjRmVheWJ5E92SIaCKFpKp0Tj5F18i9GMrkcEmFGWzVkSdu88wubDpejQF/LLtd03TpxLgt/iJP4yIaQV2j+nNx9U1FdAt9FBdOqYb5/PpIqlrRg6hHuSkhZsMV7qOuqMCCPZoL32v1bde934fLXCo7zkb4MA1SgKlgnMlQ9dd8tup/hxPWmD+hdz5z7ZZjM3Etr2yW/cLxFL2TkcK82Ar7ZHLKOrLVIg8Pc7LI66eWiUxYxHdGpUq9C4nIQRMsP1IJ3Pd+CNPI8wQu+gG3hbfxzYf2cs66Rv75Us+sLvOjGcXRTujKW3De+nZ+EPknPn6LzD+++wra6sonigsESw2v1/vjzN8ej6fSph1AcazTKHDolYNq/Hpp9xUqQpmKeo3KHmQlSSgKfYlCw7J5+tWy7cnK4XkcDDpeudnUGUc0V6xg9tyxxSNTGKGAQ9DNzIkAXSP3Hr5Ah0qRzIYq73FBufM0+WGF9tg4YUvrYYmWTyKlZAsxLAZ6IX3lOJSS64vJLp3wxtkortxaLfnewvmm+HVaXKk1U/immGrvZLjM/ssBYaAJlheqivPxz2Hue5TA+d/kHvV0vnLfHk5ZXc/X37wFo0E4hWdDca0m+PZbcdx2Fd8NfZF/uEXlxndfSX29ffadBYLlhR0o1kpigO6IhCSB2Vr5s2gxG0nOg4fe7jDPeiw9zCTAWLpfc2owW6jDIElVeX7qLEnc8kx2v9lQZBnVAOa89+yKuBesRiRJyp6PmQgYcm3OUhBvSVEsq8ViLOlAC0H+9ZsvHOYU7sDTWpXOeWrbZjdhjs9PW93+6LydczXXL87sz/fRyGzXrsEwPefrZrebkNRkxf3sDguWaBLzHAzn7L42/ffnyuirBX09wfK958tTasFRi33797Dt/i2hkz7GQ9ZL+MKduziuw8W/XbEVi1EYZ9WiuNcQevsfsd96Fd8Of4nP3iLzlb99T63FEgjmmyjgLlpmAXQTMFUV4rNUrpttfbWEQ/F5a6sYSS6o31CWxr5HmEvwXKVtzVbjgp1PLZlYpHNaiOsnR0dQqHzf5ko8GFqS9/lI7X+LwWzXLiEH5nxtzWO7qA8eqNj3dvZOHXJI6qsDPuKHETq6HBAarWDZYPH+Ecdz3yS68Uoe77iez/xpNxuaHfzHlcdi052LQ1AJxbWa8FV/xGx386/hm/mXn/2OYLnKZgLB8mQQaC9a1k5+jfIa4Q52z77RIVLrwhaCI5eG4L7ZNxIcUbgqTPxcjvpgaRXZYg4nXzB6hBtnIAw0wTLBNPAUdQ/fSLzzDJ7e/EU+decuVritfO/tx+G0CEfwoaK4VhG56jaMdjdf9n+eH/7hdqLLOGZbICjiWeAEj8eTP0Xx2enlNWVBFV1hoAkEAsGyRhhogiWPYdKL6y/Xk3KvZftJ/8HH7vDS5DDzw6uOo96+uDO7H4korpVEr74do6OBm/2f50e33l4woadAsJzweDwtHo/Hmf75GNAL/MLj8Wz1eDyfQavs+D81E1AgEAgEglkQBppgaRMYwX33tahGK6+e/WNu+FMfDrORH169jWanqDw4Xyh1nZg/eA/YGvjM5E387Lbb5lTxSiBYQjwP3Ajg9XpTwFuBVmA7cC3wNq/X21Mz6QQCgUAgmAURGyZYusRDGP/0boj68F74f1z/l0lkCX549TY6XNZaS3fk4V5J4p23Y7jlSj4xdhM/ugOufdvbxZxygiWN1+uVin53Ff3eD5y7mDIJBAKBQHA4CA+aYGmiJHHdfwOMvkLv67/LBx5WiCcVfnD1NlY32Got3RGL4lxB6l13ELe18JGhz3DrXbeiiooDAoFAIBAIBIuGMNAESw9Vxfn4zVh6H2b6vK9z7TNNzESTfO+q49jQ7Jh9f8FhoTjaUd91ByFrBx/s/wx3/VkYaQKBQCAQCASLhTDQBEsO247/wrbrV0wd97e8Y/tmxgIx/vPKY9ncVldr0Y4aVEcr0rtvx2/t5JqeT/PgfX+otUgCgUAgEAgERwXCQBMsKSz77sT5zL8QXPdmrum9hJ6pMP92xVaO7yyea1aw4DhaMLznDiYsq3n7/s/w+P2/r7VEAoFAIBAIBEc8wkATLBlMQ89R9+Anibafyvt9H8Q7EeH77zqBU9c01Fq0oxbJ3oTpvbczYuni8r2f5ukHfltrkQQCgUAgEAiOaISBJlgSGHz7cd3zQZKuVdyQ/Ad2jET46mWbON/TWmvRjnpkeyOWa25jyLKeS72f5bkHfl1rkQQCgUAgEAiOWISBJqg5Unhcm+tMNvFp8808MpDiixd7uNDTUmvRBGkMtgZs7/0jfRYPF3s/zwsP/LLWIgkEAoFAIBAckQgDTVBbEmHcf74OOTzG111f4vY+M5+5YANv2tpWa8kERRjs9TivuZUDlk1c5P0COx74Ra1FEggEAoFAIKgJ6gLOEysMNEHtSCVw3fthjOOv8OPmz/OT3iY+fu46rjphRa0lE5TBYHPhet+t7LMcywXem9n5wE9rLZJAIBAIBALBohM1LVyNBGGgCWqDqlL36Kex9D3C75o/wTd61vOhM9dwzckray2ZYBaM1jrq3/d7XrMcz/neL7PjLz+qtUgCgUAgOATMRqEGCpYHBsPCeauWIuLJFNQEx7P/ivW1P3BPw/u5qf9krj9jNdefsabWYgmqxGh10nDt79ltO5ELu7/G9ru/X2uRBAKBQDBHDEILFCwT7CZDrUVYVMSjKVh0rDt/jv3F7/Ok6018ZPiNXH/Gaj50ZletxRLMEZPFTtP7fsdu+ylc0vsNnrvzP1BVtdZiCQSCMgTsIkJBIBAsT+SlaLGIHDTBkYJ5/904n/giO+1n8v6xd3H9GWuEcbaMMZpttFz7G3bZT+fygW/z7G3/Jow0gWAesZvnb9RYrdEn32oWqsaCcpg6osUk7s9SI2jrmPc2F7KgxWLQ4rCULIuZXDWQJMdCvlPFUylYNMw9D+F64O85aN3C1VMf4oNnrBXG2RGAbLTS8r5fs8t5Fm8Z+Q+e+sM3SCnCSBMI5oP8vIuE0VZDSarDbTeWLLOIPKcF5XANLOsc78+6FvthHW+5UEuPzWTdlnlvM2F0znmf/PePIi3OBTmus053uSTB5vbCcxhpPG0xRCrLVN3mBWtbvDUFi4Kp/0lc936IPmMXV/g/wftO3yCMsyMIyWil5b2/ZLfrXN42/gMe++1XiSeVWoslECx7jLKmIC2WctTTfonu8uLwyJRs0t1uTYOdRkduXYfbCstovGaw6axaizBn2pylngU9Vjfa5sXb5kh7dRfCy7MQ5PfHajEYJFbW12ZAJGYqNVDcttKBDz3a3eX7gmHt6+csS6fbmv27v+UNc96/HMYqC36MNpxIwujI/jYVJU2q0vznpQ00n1P1tuXeg/OBMNAEC45p6Flc91xHv9TBW2du5H1nb+XDZ3XVWizBPCMZLbS85xd4G9/AO/0/5pFff4lQPFlrsQSC5Ulaf2mymwHoa3tjDYWBYs1+rP7Eslvmh2W6rEYOZ6jG5zym4nq/c91htF5KymCdfaMFJmkoNAwilqbDai+j8NfbTCXGsjSLxTZV5ym7Lm50H5Zc80nQsYaYycXGNkfJuhXuud/TJocJ+RCM2YRxdu/ipKuy12W48YyC32P1r9OM6ypoTRvrE+5jC5YnDTYctkMzOA0yjLuPQ5WrMxKrodgTVo6IpZWhxtN11y3UAEHS6Kj6ei8kwkATLCjGke247n4/Q2ozVwY/w4cueB3Xnba61mIJFgqDicZ3/owDLW/kmtDPefSXX8AXjtdaKoGgZiQN1Xk3ilnbZGdbpwu72VA21MqWl9tVrEg7LIUjy37n+rkdv9leEN6kllHkM449RcqNJBcr/c12bd2Mvbp3f6PTnP07ZK2shCXlQuW72uu9skFfaS/n7AtbWqpqtxZMr9H3emZIGJ1Zj8WaJht11uoVbaXYQ5B3axfLq1sN/vqtJIxOrMZcv5dl2NbpQtbJvRqrP75ie3r5TlBa6r2n/ZKi3K7Zrbr8Z6WYuNGZe6jShK1tSEi0uqp/lyQMhYbquHsbUtF1qDZ/a1NbHbJr/uanleXyAwNJnQESNd0H9e7jKWvqCzxsh4qSZ3yaDJI2mFEVokiIYBliHH8F15+uYSTl4urwTfz9padytZiE+shHNlJ31X/Tu+JNXBf7NU/96iaG/JFaSyUQzAtRc33ZdePu40qWxY3llaB8I2Gs/oSy23la9UebHZacUjHjWEtv6wW6Ck5P+yVIrVvY0lFXtZLntJQPHQrYOrN/r2uqrBxJEtRZTbS6LEw71pbfLk8baeg6KW/N4cVHljNGGu3mglwXk1EiZTCXKMfVsKVDP2cmg16/qIRapPMpknYO8bI5RLMriRnd1m01sbYpz8tzOPqlJBPXCceDwtCvDh0PlqdN30sx17muCm9XYV85tqM6A8TnPAaDQcrKYzcbMMgSNp2y7i2zhJP6nBuyf+cPNEB1Hp+grXyl1XyP3qZZPFAxc0NBqF7M3JC91XPNrUtufAsms3YuM47C6ZCGms4kYF81p/YqP9Hl779eLqvLamKw+ZysJ73dbTnEokQ5qc5ZX+qxzvfU28xyLu9TVHEULDeMI9tx3fFOxhM23hX7HJ9681lctqWt1mIJFgvZiP2tP2Rw1RV8IHkLe37zcfaNzdRaKoHgsFCKQnyKC2JEzY26BlIxGeW1oc7B1s1b08s0BSircOZVQy3Ou9BjfbMDVTaRMOiHWDktRtxWE44qC0pUCn2bS6hTppUGm5FKylf+GmNL5bDGysdTC5T8SgVKMuc4Y1/N5rY6ArZVZT2FemxocbCt0wUtm1jR2lx2O0U26y4vb+xrMmztqGNrRx2KbKK/5VyGms4ss/mhqXJ6uU6z0V6XM1BUpKxaW84jCZpHQg89L8WG5jkUIClq9oSV1YVcDjeehpRfbViS2NjqzMmZ/sdkkIk3bizY161j8Gfa6m29kLC1g562i/E717GyIH/r3Gw/KPFK5jHj6Cq7rtmh7d/gMGGu4p2Q1PEs9bVeQF/rhczYVzPuzg0KWfKMmglXUYES2YAzPRg0VbeZweazs6viJtecnpnZKB6cqITTaiw5cn393MOB7WZDNte3HAFbzgg9psVJS9r4XtO4cAVzhIEmmHdM/U/guvPdDMXtvDfxBT79tvM475jyHy/BEYpswPzm7zJ0zPt5l3oP47d8mKe7R2stlUBQlrCtveL64YacYret08WahsKPs4rMaMPJBcsM9foj4gPNryfedSHJttcx2nASMXMjnjYnXekPvs1cqAge01reU+W2G9nQkl6fHtHN5IGN1Z/A+hZH1gtXZzUV5H/oeUB0c3byR4p1p9JQswU29AaVzWUMJU9boSdgeI5V2bZ1lnpJDHkCqHnGS7mQvFSBAVUo/FSdh6Em/RyY7P5Nm3GtLu8B1aO39YKyVfVG6zUPokGWMMgSimQiZbAVGGIFYXV5y/taLyhzxNKbosiWrAFbzhNWXEbclWegRCwtSJLE+hYHjXZ9IxRyhlhhLp2+QmzMc+9YTHLZipFum5G1TfZsK3azEUsV3ree9kuImRsKF6rli6VvPbbwebYYZY5dkbtWugVIJIlta3LRQgmjjZTBxjFbT2PCfSyRKsJlVanUEJQliW2dLlali5fMxcBuq7PQVmdBkU0osokp1xaSRi2MudlhZmOLg1aXdg+DOqHInlYnJ61yp89n7tUgIedJzdylanIrKzmotrYXPvub253I7pWMthQWQ1nfomOotpwPaKHg61vsBVEKxaGg9XYTm4veUw12M2uabGzUaXu+EAaaYF4xH7gX193X0p1o5oPSP3PzOy/itDUNs+8oODKRZEwXfZXREz7JW+UncPz5em7bfqDWUgkEZZDJDEw32EsVr4SpjkCFECRFNhcq++vfwLYtx+FrKs13aW5sAIMFJDmrsFmMMnUWI9s6XQUjuomOU/Sl7dCMgoa0AnzqmnqUdFWz9nQoY9jaTnudBUNee/keuSGdqoXN6dFhiZxRUzxKnjEQMk2F7StJpBXGuvRo+1j98ajmunRbEm6bqcTTUjxynSmQYchGEBUag6Y8Q89t17yC5cpyjzacxOSqy4iYGwEYr38dfa1vYKJ+m+72oBZohMHW1zHjWItqbaA+3R8mXZsL8pdUk2ZASHmGhd+hhUO11JUaLZnz1/JqyuThGB0osnYfk02b8eeFzZWROku+d6a42Eg+U3UbGXdvo6O9E7vZmA2jLCZhdDLu3kZ/y7npJXkGr2zGIOeqOkJhiF8xKVk/PDBiaSIlm7QQwLxLEmjchlzmGq1utFFnMWYNuhNX15NsPlZ3Wz3UAmO3gpdVLgxzTDUU5nKub84p6O48I9WWDhFe22xnukHrb0ajUTeEsbO+/ICIIleqUljZID29q4GhptMZajqDE1a6s++A/LlKt7bXsbrJgYSEw1zaB5IOzdCUJYmGvPOz6IQRVrr3XU22rDGbOXrGLC4OBc3njZtay67LPHsZ5PT56XkOC2jdgpLOVW13WZGQMMkSW1fUcUyrA6MskWzJ9aXVDTY6GwuNQUmScFtNBc/9fCMMNMG8YfHeSt29H+blZBc32r/Gt957Plvb5x5CITjCkCTks/6BybO+yhsMOzjh6Q/xgwdfFnOlCZYkxrQB0Ow0Y8/Lw8oU5Aim8y0UW2FUgJbcX6gwpdIK8mnHlRoEevkt5VDqOguKFqxssOK0Gmmsb+C4zjpc1nTIpN3MZN1W/M51SEhZxafYCFINJuoqlO1OtGoGSKvTgi9dfCRmyg20Rc0NrHBZWdtsx2yQ6W27CL87V5nOIEtsbHPQ2rkedf1F2eWnrKmvIvleey9sbHXS1WQjZXQQNddnjaz8M1nTYAdJQkJiczoPbLJua3Z9xNJCV16+ldtmRpHNnHRsYU5YxiMQNxZ+ryzG3HXd2KqtS8lWwunCJSoQX30uxfjrNjLp2kKHq1Txzvc05Ru9Evphgqn6dbqlxFVJZtK1mYB9pdamrq6ee8cWr55xrNMGFFaeReNKD471Z+FzbiBiKY12CdlWlC1sIef1rf6WcwnYVtHgMLFKx+gIW3JpDsWekf7WC5hwH5+Vc6D5HGKWRl3Pa9JgKQnBNcoymLTnTW++wIx3KCdLK37HOgL2VczYtbwqvS9SsTcl5S7No1zTZKPOZuS0NQ040x7GzF51FiPYK3vMzEaZgeZz8oxgjd7WC6sqbb+hxcFI4yn0tF1MbN0lWS+q22ZiU9dq7O7m9LmQXT4bmQIieuHMkoSu5ygT8Lo17WHMD/cu7n+x9Zchy9DkNBeEgk64toHZRcjaylDTGVQi1ejR8bDN7kV1O6ycuqaeerupYHDBIOXlHRYb7QYTKYOZqLme2LrKRXnmC2GgCeYF68s/w/XgJ3g6uYlvt36d77znbDrdtS9TKlg6KCdcx8yF3+VkeR/v2PMRvn77Y4TjqVqLJRCUJfPxX9tspy0v96a39UISK8vkAxXsLxX8ezjUWY1ZT1md1cTaVjeqtbFEUVUMFvxOLW+m023hrHWNWLNKh7ZtqnETqyvM8aTUa0pok9NE08pNRDe+lbC1jYHmc+hrvYCwtYNN7XVZT5kqGXIXK/2v1WQsW9ykHGuabJzepYU9mQwyLqsJSZIZaTydaMZAK76UaW+AKW0ohK1tRFaeg7z+PM5Z36SF8KV3OmGlm4s3t+auR5qwtZ34mvOzhleGlK2Js9Y1sm1FhWIT2aqRhYK1r9lU8LtcCGGGtc2VwwSLUZFZe8xxtHvOwGKUS2SMmXL5WJKqlg8Vkw0kW49nQ3sjp59yJmPuE4iaG4oqE4Itfa8lSS7ocxubcsq1FoYpsareRpOOVyRsbaMnPV2EUZZQjZaSebsyh814QYyyVBDGGrE0MdhcOp9XZr/YMW9hsKl0ff7zq+0gpw3prSDJVRXO6Gt9A6q5tE+7rSbWNmqDBaesquek1fW6z7xuZHBa9qTRkR3QyW4vG0uMc9VcV/IQ2M0GFHsL9XYzGK0FXtQOlzUbxSRLEqd1NXBSmVy9/GbH6l8HQKJujf62uoaQdoKZEOOhxjMYajpDf64wg5mTV7kL5lkDaGhuJ951AeP1JxI3zZZTKOn/SgXPjAwAACAASURBVB+/3W1lQ4tDR1KVBruZ1Q1z01H7W97ASOPpYLSScmZCWEWREMFSRUlhe+xm6p78IvelTub367/Ft646NTuiKxDkk/C8jcCb/5cNpgluGv57vv5/tzEgKjwKlhL5tQNIV/Yr3kQ2lo6w5mk3mdH6/PCX/IlZZYk5KwcAaxpsbGhxYJIl4msvBkP592xs3cXEu96YTe4vRllxCl2bTirIz1jbZOOsdY25U0LC01aXHVXWQu/SYUrp8KJinTPRpil2xR5GANLhnzP21UTTeUCZy9bgMGNfdzZOp6YAJzpPJ9F+cnZ9xFI+1KmYhLmeY1avzubhndDpxlM0P1bKVZhno1p0lEGDGafFmA0NO6bVwdYOZ1Ym1ZRrszhMzmwsVKyHG07Nekg2d9RlPS0A044uEi1bUaz1BV5bPTLTJfgattHhsupOpjzYdBYjebmQqiQVeE1i6y8rX2rfYGSk8TSCVk0BzYQ+nrLKTVeTdizVYMbZtpbT1jRgLRI3Y1CkGgsN1GzhifR10kqmS7Q6LZy8spzxWqj8DjSfw1j9iQVGS4nNI8kFYbC5lioUqJFy6/taLyBQVxjGGLCvZLThxLLFXvIxG+VsMQ9IPwc6hx5uPJUJ1xYC9pXEGsvPi9bVaOeiTTkPXLzrAuKrcgZopnDR+cc0c1rX7Okk9TYTxioKjKQMNtQtb6OxeQ7F3fJuxoYWB69b24Zirae/5Xxm1l6erULZpDMQ0eaysKHFwfGd5Y0y1WBGcWp5wpnHLVN6P2JuQZJAcbRmn0+n2YDdbCgdnNCN3plbRE+y/SRiay9e0CqO8zfrnOCoQ4oHMd9zA87BR/hJ8lLGTv4sXzhz3byMFguOXBKrzyN41e2473wf/x76LDf+3ygXX/YezlzbOPvOAsECku81iLadwpjDwlhYZX3yCeqsRjqcVoanoxXbkI0mDGntQSKnSHpanewZDaAoWviePIcQx2zbspQ3CXT596zTYgSdMK98FNdKLK6VbAAGrUbsZgN1VhMxHYNuXZOdg5PhwoVpz1FJ8bNK85DJBmIbr2DqtXEAukbuRZYktnbUIZksJJw5D5bi0BQxdVjbdnPXSl42XcKa4HakxHh2O9U2e6EBk0FCNhpIoGbVsGT7iRhm+mbdN3sctPAno8PCsTYXu5PnsrYrV1RGtTWjSlLZCbxV2UQKzUgyyRJWo4yS8WjKFpKNG0m41rJqRYLd+8pXvPU7NhCydbJ5dfmCNplcwNGGk6gL9+NzbuSU9gBM7gHJAAYzZ65tLMhFymCUJRIplSnXFoK2lThdbjY0O7AaJSxWE6okEV9/GdmJFoq+96qk3WONp7RlFhfBVGnhCdXkQEpGUR0tECp7OoCW36SXWzTedAqhlIn87LOz1jVhTh5a8QZFNhFweiA2kl026ao+t60Y1WSDZPrvPCMgZm4kZm6kpclO3GmGab/u/pKkGSGx9Zfqrh9sOostnYceraQ428EqQWi4ZN2mtupSVNZ2bUDqH2HU2KDdR0nC4LmMZpOZU1bX88pQgHq7FpaayReNQ4FLMePhjOm03+G2YnKeT7yjBYxWNs8MIwPJvL4XMzcQO+YKJEnCYvQz3Hgqx0i7ALCaZFLHXEo0qcLwS1jLeAX1iJlcJNpPLl0h5UJqFwrhQRMcEnJgEPMtb8E++Bj/pPwN7su+zvVnrRfGmaAqUs1bCL/rz9C4nu/xTV648zv87Nk+lHIxIALBIiPJBi2RXJJR0UbYi8PIkq3baFqZK8MtSXDWumYm11xOT1vh6KpBlqoql18J1VIaapdYcSqJjlOzv89e38iJq6orN55hXZO9oHx6MUaDXLZUukSZSV3LfAtkSSoph26QpbLfjpNWuWl3WWits9DkMNO+9Q30tl6YXZ9/TSZcW/WaQLFpgz+qcfYpEKphhdvKhceuwWzJu2aShPHYK0sq9G1sc7BFZ86qDc2OvBy09HtPNsBsMkoSr9+ypiS/LdZ1EfHVZxcsSxjrmHJtQZWNKE0e4p1nEuu6IK8pveCvjNxuNq/r4rQ1DTQ5zJD21KSair09RaGQ5uoHHlSjjdi6i0u8bXpMlylAETU3kTA6C7qbxSgXTFhdLRkvWnEJ+1PXFE6HkDlWf8t5JS3MRludpWDqh65ZyrRnz8tg0R38SBlsqI5C7/LrNzRV5U0DSKw4HXXd+VVtm5NJIlWfy8VrbltJ08nvIpae81GxNmWNF5fVxFnrGrNeOyn9Pyj1Ypdj2woX61atyj4bpnR1Uz25AI7rcLGpzZn1/EtIbOlwY7fZGK8/ITvxdQbVaCPRcXLRMu1YIWs7iqt8YaiFRHjQBHPGMPIilruuQ4lHuMl6M++48r2zvmQEgmIURzuhq25Huu+jfKXv5/z8uSFuGv4YN1+2tWxYlkCwsBR+9GUdBba1zsJYQBvnTdWvo8XeSkNskJeCTtY02AvzvYp218LgFFL2Vt3R0fiq16MaLFh6HtCVLtF2EpbgnwuWKc4VBb/1KrHNB00OMyMzeuPbxZQOsqiyESVPGSvJBwL9UWq0Ah2Z6nEnr67PtqdHxhArJtW4CcW5oiSMUZWNBZMHb+t0oagqRrrYH3agn0FXfhCp2WGme7zQFWQ1Gmits4BP+x1ffS7IRtzmXBVHicKu0ugwZStpgmZwxJIKgKbs6s3ZZHZw3Pp1DO0ZKytfsSJfic56a+GAgiTlecYKWs3+ddLq+mxeYiUc2TBOtaKntzgPDjRjaSqcgKnC5cUhjKrRhpSMaF66xCzuuYxcZgObW+rocFmgO7e8oSgkL2tgVDHnobZ9jhNWutk3HuTAhOaR1jM09I6Vj2qpJ+VaxURM3wizmQyzFiE6vtOl+37L8DqdPDWDLLGu2U57nZWktYWeycKcTcVgYbThRDwrNpbsC6BY3Mix6dxv1ypirlVI4XHkyCQUGU56VVCrwWSQaW9bQUrtxBAYzC4v7SNWpGRUCxkt8oYpzhWMNpxExFy7KaKEFiSoHlVF2vkr6p78EiNKPT9e8V0++uaLFkwhEBwFmB0E3/RT1Kf+mQ/s/CnHDfTwqV9/mhvffBYb51hgQCA4XDa3u+gP5H5n/Rt5OvkJnS5Seb9VsxPj2nNYqzqpd6RzdHSU+PjKsxmWYoRj/5+9+46PqzoTPv6709Rlyb2CGxxMLyF0k0AIZIEESAIk7CbkTdhNIL33uiRZ2GSTTTbZJBvSQ4A0SuimGmyKAdtg+7jItmT1NtL0et8/7h1pNJqRZiTNjCQ/Xz5Gmjt3Zs5c3XKee855TpxlC5eQbfSYaQcYmRWZIWOMOSu2sSpzpt3GeMLSejDtSCStMhRde9mo16xdUEODPy2jYUWuSZvzt3b5Era1ZukeaBhZx5hF115GXb0fp52sKNUq1cbr8bcNUldgj5CGKjeN1W76g7Gc65iVw5XqoxfW0rl/9DqnH9GIJ1oFSatv3Hlr5vGotrp1Fvvm1elHNNA+EJlQa2/62Kt0ZtrYrfVr5zFnf343dFNzgcXnHg12r9Y5VW4aqz3EPKfiHDiAafc4zvxTRVesxxHqxdW3a9T7qkW16E5/1s/MNTZULaylPi3zaU22sYIj5qaz100LOjI7iKyaVz0iQKutdHHS0nqeacqIPrN8TnzxacT6cwfj41mcJcNoqiT1VS7rpkIWRy0Y+7ocqlgIWcbtAsSWn03FvgdGLTerF5DIyHR5/tp5k+txYDiILzk9LUAbfSzHF5yAq/NlcFnf1XTXkKweDsgyW8OPnFuVRxbaqSM1a5GfWIj4g59hSfPfeSp5AttPvYWPnn28dGkUk+dwEjjvG8QXncIpj32GX4Q/xcdu/whnr7+cd568RPYxUTLzaz202L8n3VXUVrjo9EVGdO8zDANXZstYzUKytd2kr2ZWzyfh6CXhTIxKMHLs4roREwDHlp+L5+BjGPHyJdCJLT0Dh79tzHVMpwcjEcXtdBAzrRYIUolZxzlu18yvwah5M1Gnx0q2UcBxfvaquRCemu6K2W4EpYoyolenXYkblRwmw/FL6tneNmhNCuybjyPUQ66ub/NqKwhWuujPkmXRrGzECHaD4Ri3lWUsbzhqfkFTmtRXuqcsyVfC4caZjBFfdBL4rX3Z43RkbRVaXF9Bx2CEyKqLaYna0ZjDTWTNpVb3yu7uEesn64+w/u3pAbJsYXcVSfdyyBKgrZxbTVNvkJjdKpmP9OkakpUNjDdoLlm7hPiCE0jMORIjOPKmQeqalkoc5LaTCdVVuEYH4GW4/OWae27SxhqfmiEz0+pY6qtcDIbiY6+UPu+d/TNZt4xo3dBoSqKrLmIs+Y7JmyoSoInx9e4mefe/siS0l1863skRb/0KV6+QhA5iakWOvoL4vHXU3/8Bfjt4M999ch+fPfgevnyxymveFiEmy1x0PMGqNjrmrGaZp541tU7m1ripOVTYpbLQoZQrMu/aO92YntqSB2jpiQiStUtI1i4ZY22sTJKYHJ904DdhjtsB/vy/vJmjS+J46ipdVGRpram1e3PMy9GSk69FdRWsnFvN6vnDlfL4/OMwPXXjbpNqj3No/E9syetweptIVi9kqAkoq9HbLLb0DIyo3xqXNgkVWTIaTqVEzRJc0T3Zx/fZgYg15sfal9ODzfQsmCcurecEu7fuiHTzGa3GmaHD3Co3nb7IiPnYii22/Dw64t3ZM1qkGMbQpNar5laztXVwKMHPqrnVJJKmNY8f1v588vI5WffbstyeLOBDK93D+9dJy+pLMjwhtvQMnIMHhx6fvqKBcD7B9gy71ysBmsjNNAm/eBsLn/82AdPNjxffzKWXXyfjg0TRJOYpBq+5n9oNn+JLTX/kkZbdfOg3H+KmN58+Iv23EEXhqqSv8RRiEasZyDAM5lZ7iC0+Le9xLDBcKc5VOZ5Uo7BhWBHgBN4kVSE2c437KeAONzAUPFQ4YFFDNV5veqbHElaYF56EYSaoq3TxxqPmZ53cuBAOw0AtymhZc7hINKwu7I1clSTmH5v7ecOwO4dmCWodLszKiXf7dDkN4oniJ11KNKzG1b+H+KjkIXm8dt5wchDDGCsR/rDMHhUnLK1nbSxRcHe49HfxOB2FZVlPJRDC6sqarGzEEe7Pufri+soRXQqdDmPUHIHZxmWCNVl8KUTWvAVz571A/kfuhUfPH/H3yNZtshgybx65nA5qx/v7p5UzWxf0bM5a1Tg09rMcpKYtsgv2ELznYxzZ+yTPmCfSfu6tXHNy9gxZQkwl01OH75KfEd/2Sy589mZel/wkn7z7X9mw7hI++cY1coNAlFyyfkVB6y+bU4nLYeSsdJVLomE1SU8dZsZ4j+iK83AEx2rhKUAZErEm0zLKTTY4K5Zz18wdmsB3WFrX2XGqxacsn5Mzk2a69WvnEYknqXY7SZQiK667ishRbxv3hsGcKhcDmd3Qxukumg+nw8jrmhCfezT+vuGEEY12yxvY+0zuYYPjii07GyMeHH/FAl28buykLmcc2YgvMk7Xvnw5K4Za/vO995PPfGrTh8HiugoO9AZZUJPfebnc8/lKTUeMZJoEtt7F3Ge/Tn0yyG01H+C0Kz/L+Q2SpVGUkGEQOukDRJefQ93DH+FXfbfyx91b+H8HrucTl5zMWSulNU1MX4ZhZL2bPB0mkcgMzsCaSyyRx3xiYHVpyrz7n/VzZlp/oiLLlkzLdNcwr8bNgXgV9ZUuQoFElldaciVtyJRPBr8pl6NGP7fawxy3EzA5/YjGvMfCnX5kAy8cHDkv2Or51UOZDwuR9NTjjPpJzFlFz5zhBBAnLK1n/mCY19p9Y7w6T043prOwqS2mQkO1m4bqqQsiBo+4mGbDS2MRj93I6kuKOrlzTvZk7eMFvdOJBGhiSHKwDd+9n2Kt92m2mWvZceq3uPSsc8fM3iVEMSXmrcN79T+o2XwL73rl51yU3MoX//ZeNhx3GR9dv6rsd7jE7FPMRoepGSZjUM5Qb+2CmnG6Mk2HMHRmSNavoGptJedUL6DC7aR8KWGKY/mK1TgHDhAxXDhzzF2Vr6MW1I6bQTCb+OJTSYRX2WnUh4Mxp8MYmhIgWxUnPm8djuDEsyTORFHcJJ0VebXUTtgUzUV4OJhJ7ZOiWBIxeh7/AbW/O5/l/Zv5Q/0NOP7lfi48+zwJzkT5OSsInPMVvO+4hzmNC/iF5/tcsuvz3PTLB7n31Q6SBWQoEyJfxTj1nbJ8DivnVec1NUl8wQljrzDdJ3Uv8rUjtujkcRN2zATZWjRni/jCE4msvris00PgcI27jbNlLUzMU8RWnJfzNamxYcVqrSxqkJRD3L6Wzqyui7OXtKAd5gZ3P0HlE19iXewgzxqn4j33G1x04smS2lxMO/FFpzBw9QNUvfIz3vz8f3FB8mP8fMM/8f9e+2duuuD4Cd1dFaKUajyuvLoHApgV9VmXJ6vn4wh0laebUD5KFDgm56wkOWdlST5LTJDhyDoRdWzJ6RjR3F0LS3Xvob7Sxcp51dbcZ/shMefIvF+7cl716Mm8p8j5a+dNqrVxopbWV9AbiLJmngxpmQ4kQDtMBdteI/joN1jne5ZD5nz+vOa7nHHhtRwlk06L6czpJnTah4kcfSXVm77LR/b8jXf1PM5//eEdcOK7+cA5kkRETM40b5cituT1VkbJKUiwUIi8t4tpj6MqcfnEzJFMm3uqnAxjOJti5OgrCn59MYIzKGwOsKnkcjo4ZXnpx9KJ7KQmc5gJ9zXT//DNHNdzP0EquWfe+1l98cc5f25juYsmRN6Sdcvwv/lHhE/6f9Q/+y1ubvslu157mB/tvJpVZ76Dt5+8rGgXTyHKyuHCrJjGlSjTTkstAZqYoBp7vrBSpW0Xs1d0xfoxW2unMwnQDhMD3a3s/fu3OaXrzyw0TR6pvZL5F32Ws5YtL3fRhJiw+KJTMK5/gIGX/swRG7/N9/3fY+ezd/GTLVdz3Pp3caFaKN11hSihZN1SzIH9xBuPLndRxAxV6XbyJrWgLN38xOxiVs3FrJqZWZ8lQJvlulv3MfDUDzml917OIM6m6gtwrv88p60tfFJJIaYlwyC65lKiqy4mtvtulm/+L/498J/se+QP3PXsFRz9xvdx8qql5S6lmCGW1leyrydgTV47Hcy0lihnBdEjLyh3KcQMJ8GZONxJgDYLmabJrh0vYzz/I84MPIYJvDjnYqrO+ShHrz6+3MUTojgcLqLHvB2OvoLEnnupf+4n3OT7Kd5//JbHqt9C4zkf5LijlbSoiTGtmV/NynnVuKZBBTG2+DTMSul+LoQQhxsJ0GaRroEAuzf9leX7/8T65FZCeNiy4Coa1n+E09cdj9c79TPdCzHtOJzE1BVw9NvoPPQcvo0/5m19fyX5yN944akz4MTrWPO6S3E45fQ3kymlKoAfAe8EIsD3tda35Fj3YeCijMVXaq3/nrmuYRi4yh+bAdY8WdPF0QtqME2TxXUyLkgIIYpNaigznDcU44XXNM7X/sh5vvu40uij27GALSs/xOLzbmBN/cyZNV2IKWUYOFacyZx3nUlX3wE6nvwJ69ruo/HFm+ja8lWal1/BknOuxz1vVblLKibmVuBs4E3AcuB3SqlmrfWfsqx7LHAt8GTasv7iF3H2qHQ7OWnZNE5OIoQQs4gEaDNQXzDKRn2I4Kv3cqL3Ya42tuMykuyrO519p95M/XFv4QiH/GmFSHHNXcnyK28hHP0mT2z6CzU77+C05ttwtPyS/dUngnor9SddQbJmcbmLKvKglKoBbgAu11pvAbYopW4BPgz8KWPdemAZ8JzWuqPkhRVCCCEKJLX4GSCeSPJqu4+t+w7gaHqUdb5nuNaxlRojgrdiEW2rP0DNaddR37im3EUVYlpzeSo57vzrMNe/m6f27KL/+T9wbP8G1Mv/TvLlm2mvOxnHurfiOeZSknWSWGQaOwmoADamLdsIfEUp5dJax9OWHwuEgeYSlk8IIYSYMAnQpqFYIsnu7gDbWr107XuZ+s5NrOdFPm5oXEYSX8U8fEdcQfSEtxNfdiZVhoNkuQstxAxiGAbHHb0Ojv53vMGv8bstm0nsupuzBjainv8GPP8NuqvWEDnyAmqOuZj4kteBtEpPJ0uAPq11OG1ZJ+ABFgDtacuPBbzAn5RS5wEtwNe11veXqrBCCCFEIaTGUWamadI+GEF3+dnV2o2vZRvz+l/mNHbwXscu5hhBcIC3di2+NR+Co95CfOGJOA0H8fHfXggxjoZqN5ecdx6cdx4HeoP837YXMfc9wvH+53jdzttw7/oFAUctXQ2nYq44i/q152EuPF4CtvKqxkoMki71uCJj+TqgFrgHuBm4ErhXKXW21vq59BWdToOGhuoiFHf2czodsu0mQbbf5OSz/aprPACynTPIvjc9SQ2jREzTpC8Yo6U/RFOvn+72g0S7d1Pr1awx93OicZCrjFZcRhKcMFh9BPGllzG48mxiS88iWbes3F9BiFlv5bxqVr5xPbxxPZ2+CH/a10Jwz2Ms7NrIyT2vsbrvKdj6H4So4mD1cQw2Ho+x6ETqjjiFuUvWYDhm2JxVM1eY0YFY6nFmutrPAzdrrb32461KqdOAfwNGBGiJhCnZbieooaFatt0kyPabnHy2XywcY0FthWznDLLvTc6CBXVFeV8J0KaIaZoMhuN0+cL09vcR7GslMtBBdKAd/B24Q10sSXZwpNHJeUYnlUbMeqEDAp4FhBrXEVz6NsyFxxNf8rqhZAWJMn4nIQ5ni+oquOTktXDyWkzzBloHwvzlQBORA8/S0PMia4PbOTXwW1ytSXgJBswa9rtW0VtxJMHaI4nPWYlr3hqqF66msbaWuTVuqt1OmYdtarQCjUopj9Y6ai9bjNWK1pe+otY6gdXFMd1OrHFsQojDxAVHLyh3EYTImwRo6RIxktEA0bCfSDhAPOQnFgkQC/uJhvxEQn4ciQDRwT4cUS+u6CDu2CCeuI/KhJ96/JyMj2ojs+cNRB0V+KqXEq0/ioG5FxNduJbknFXE5ynMauukMfpVQojpwDAMljdUsfzk4+Dk44AbME2TJp+P3gNbibZtpaL3NeYH9rA6+CT1QT90AXsgaRr0MIcus4H9NDLomkfAPR+qG0m66zEq6zEq5+CsmoOzqoHK6nqqqqqprqqhssJNhctBpctJhcuB22lIgGd5BYhipdl/wl52LrAlI0EISqk/A11a6xvTFp8C7ChBOYUQQoiCFT1AC0YTbNjdTSyRJGmCCZgmgElFpZtgKIZpLbCez/jdtN8naZqY9usxrccJ0ySRNIknTeIJ+2cySTw5vNwRC/Gx3q/RmOjFQQKHmcAwEzhJ4iCB00zgIEkFEdwFtFeFqMBv1BJ01BBx15OoWcpgRQP+6rk46xdT2bCU6salGLWLSNYswvTUgWEMbXAJxoSY2QzDoLG+nsYTz4MTzxtaHgF6wv1Eu/cQ6NxDorcJh7+D6lA3KtJNTewAdREvjoiZ+81tMdNJBDdRXETwEDTdxAw3GAYmDkzsn/bjJAYY9nLDARiYhsFQSGeaLKyrYG61Gxj5+eHj30PkqLdN1eYpKq11UCn1G+AnSqnrsVrPPg38K4BSajEwoLUOYY09+7lSaiPwAvDPWMHcB8tRdiGEEGI8RiogEkIIIWYKpVQ18FPg7cAg8D2t9ffs50zgfVrrX9uPPwx8HGtC6+3Ap7TWT5Wj3EIIIcR4JEATQgghhBBCiGlCUo4JIYQQQgghxDQhAZoQQgghhBBCTBNFTRKilKoAfgS8E2vs/Pe11rfkWPdh4KKMxVdqrf9ezDJmKUchZT4G+AlwJnAI+ILW+i+lKmtGWfIqt1LqCeD8LG/xuNb6gqIWMosCt/d5wA+AY4A9wGe11g+Xqqxp5SikzBcA/wkcjZV57hNa6xdKVdYcZaoAtgAf11o/mmOdI4FfAOcAzcAntdYPlK6UWcs0brnT1l2LNdaoLjOrX6nlub0vBL6LNalyK3CL1vqXpSvlqPLkU+bLgO8AawGNdf4r6z4yUYUc04eLbPuAUmou8DPgYqzpDL6mtf5N2mtOAv4XawqDncAH0893SqmrgW8DS4FHgBu01l2l+UaloZRag3WdOhcIAHcAX9Jah8c7ryql3gj8EOuYeh74gNZ6b9rzHwE+B8wB/gx8WGsdKMkXKxG7XvVjrHpVL/BjrfWt9nOy/fKklPo/YK3W+g324wkfm0opA/h34AbADfwS+Jw9hcmsoZR6N/CHjMV3a62vKMe+V+wWtFux0iC/CWtS0C8rpa7Nse6xwLXAkrR/5bjY51VmpVQt8ChWYHYS1gnldqXUsSUsa7p8t/VVjNzGbwLiwH+VqJyZ8t3eC4F7gbuAE4E7gb/bB02p5VvmdcCDWCe707CyyW1QSi0vYVkzy1QJ3A4cN8Y6BnA31sXxdOA3wF+UUqtKUsjsZRq33GnrrgDuAyqLXa48ypLP9j4Kq7x/A04Gvgn8j1Lq8pIUcnR58inzsVgXmZ/Z6/0e63hcXZJCTr1CrlWz3hj7wK+BeViVlG8CP1NKnW2/pgbrmr0Z63z3NPAPpVSd/XzqXPLvWJXveuC3xf4upaSU8mBdpyJY+9N1wBXAzeOdV+3z1j1Yx9LrgA7gbqWUw37+KqxtdyPwRvs9vleyL1cCSik31j7UjHUuvAn4ilLqOtl++bNv+L0/7fFkj81PANdj3cC6EngX8Jlif48yOBbrOpxeR76+XPte0VrQ7B3iBuByrfUWYItS6hbgw8CfMtatB5YBz2mtO4pVpvEUUmbgPUAMeL/WOgbsUUq9GTiLEs+vU0i5tdZ9aa8zsHaq32it7y1hkVOfX8j2PgdAa/1d+/G3lVKfwjqZHCxRkQst841Y8zJ9zn58i92idhPwhVKVOcWuVP8RGG8irTcCCjhPa+0Ddiil3oR1wv9ycUs5WgHlRil1BfBzoL3Y5cqjLPmW+xrgFa31t+3He5VS52NV7kp6XBZQ5hXAj7TWdGa7SQAAIABJREFUP7Yf/6dS6otYx2NTEYs45Qo8pme9XPuA3TJ0OXCUfWd4ux2c3Qg8i7Ufx7AyZCaVUp8ALrWX/x/wEeAvaZk13wM0K6XWpt9pnuFej3UH/fVaaz+wUyn1FeD7wD8Y+7x6A7A11XKrlPp/WBW9C7BuBn8c65i7x37+g8CjSqlP2581GyzDan24yZ4iY69S6lGsXj/tyPYbl30++znwTNriyR6bH8dqLX/Sfv5zWL0nvsvsciywLTMOsettJd/3itmCdhJQAWxMW7YROF0plRkYHguEse6alFMhZb4AuMcOzgDQWl9Wpm5JhZQ73bVY3QW/VMSyjaWQcvcCc5RS71RKGXZFvA7YVpqiDimkzKuBTRnLtmIF8eVwHvBwHp9/JvCyfSJK2ZjH64ol33KD1fXqS8DHilqi/ORb7juxgoF0JuVpAcyrzFrrh7TWnwHrrrdS6v1Yx0Xm/j4TTPT8OVvl2gfOANozgqn088KZwDNa6ySA1trEqiSmPz80tYHWugXr5lq5zivFoIF/yqh0mVj713jn1cztEwReAs5SSjmx7rqnTw2xGesm+ylT/SXKRWt9QGt9jdY6ZF/nzwHWAxuQ7Zevm4En7H8pEz42lVJLsW7IpW+7jcByu+VoNjkW6xjOVJZ9r5gXnyVAn9Y6nLasE/AACxh5h/tYwAv8SVnjjFqAr2ut7y9i+bIppMxrgJeVUj/BavJtB76qtb6vVIVNU0i5030R+KnWurPI5culkHI/jdWN9A4gCTix+vjuLFFZUwopcyfWvEvpjgTmF7WEOWitf5b6XSk11qpLgLaMZdm+S0kUUG601h+y13tDcUs1vnzLrbXenf5YKbUI6+bJN4pWuNxlyXtb2+scA7yKdTx+Xmu9v3ilK5qJnj9npTH2gfHOC0sYXbnpxOqqls/rZzytdTfWHXMA7C5OH8a6fuWz/XI934B1w2boea11XCnVyyzafhkOYY2Hug+rO/UPkO03JqXUWVjdEI8HPpX21GSOzSX247aM57Cfb5lcqacHu3vyGuAypdQ3sRqw7gK+RpmO3WK2oFVj9cNOl3pckbF8HVCL1d3uEuB+4F6l1BlFLF82hZS5DqsPrhf4J6zA4e9KqdOKWsLsCik3AEqpc7Faz35UxHKNp5By1wCrsPrxno412PKHSqkzi1rC0Qop85+Aq5RSb1dKuZRSlwJvxar4TWe5vmPWfUlMHbt7yl+xTub/W+bi5KMD63j8CPBNpdTby1yeiSj4/HmYyrWdPHZ3+fHOG4fjeeX7WHfJv8Dktk912uNcr59t3mb/OxVrjLxsvzEoK6nPL7GS+vRnPD3V2242nh+Pwmq0CgDvwKrfX4d1DJdl3ytmC1o4y4enHgczln8euFlr7bUfb7UDnX8DniteEUcppMxxYLvW+ov245ft1r9/xSp3KRVS7pRrgMe01oeKVqrxFVLuzwAVWuuv2o9fVkodh9X/97LiFXGUvMustX7YHpfze6yg7CWsVsCSZ8ssUBgr01C6CnLvS2IKKKXmYN0tXg2ca3eTmNbsc/bLWMfj8dhjGcpbqoJN5Px5OMq1nUJaa1Mplev51DYc7/lZww5Yf4A1Pu8dWuvX7O0z1nk11/bptZ8jx/OzbvsBaK1fBFBKVWMlZbgN2X5j+SqwR2t9V5bnJnNspm+7WNrvMHu2HfYxOl9r3Wsv2mofx7djZW8s+b5XzBa0VqDRbjZMWYwVNfalr6i1TqQFZyk7sQaMllLeZca6w70rY5kGjihe8XIqpNwpb8G6U19OhZT7dKyuVOm2YFVmS6mgbW0PGq0HlmmtT7cXHyh2ISepFes7pVvMYdbVq5SUUvOBx7H25zdorfeVuUhjUkqdaCeISLeDMnXfnaSJnD8PR+OdFyb7/Kxgd2u8DfgQcI3W+m77qclsn1RFb+h5e3zkPGbR9lNKLVNKvTVj8Q6sG5ztyPYby7uBi5VSfqWUH/gscJ79+2T2vda0x2T8Plu2HQBpwVnKTqxpBdoow75XzADtFSCKlWo25VysrHYj5iVSSv3ZHsuV7hRGB0DFlneZsQbDn5qx7FjKU/kupNypyuAa4MnSFC+nQsrdhpVeP906oNQV2UL262uVUj/WWse01h323ZjLsCri09lm4GS7u13KufZyMcXswOA+rOBmvdY62yDl6eZq4KcZy07DuqDNNAWdPw9jm4FlSqmVacvSzwubgbPt81yqFensjOfPTb3QTjBwBLPvvPI9rMryVVrr9Jug451XM7dPNVY9aLOd3OGF9OexEhTEsVqwZ4t1wF+VNa1OymlAN1ZSBtl+ub0Ba+zZyfa/XwAv2r9P+NjUWrdhJfBL33bnAm12MpFZQSl1lVKqM+NG3SlYw5jKcuwWrYuj1jqolPoN8BOl1PVY0eOnsboAopRaDAzYqVTvAX6ulNqI9UX+GevLfLBY5ZuCMv8M+KhS6j+wUppejjWHzutLWeYJlBusgzgG7M7ydiVTYLl/DjyrlPos1oDhNwLvwxr/N13LvAv4Tdp+/QWssXS/LmWZ86GUWoDVVcmPFbgfBH6tlPo6VlB5JmnzqkwXGeWeMTLK/QmsSsglQMDehwCiOm1ajHLLKPNtwCeUUjdj7c9vwUpsMuMy8o13TAuL1rpJKfUQ8Ful1Iex9tnrsM7FYJ2Xvwv8yL7hegNW74HUVAU/BZ5USj2DVaH5IfCA1npPCb9GUdljoj+Oda5/Me1YhvHPq7cBn1FKfQlrLqavYFWMN9jP/wT4hVJqm738J8BtM+3cN44nsVrMfq2saXTWYO1TNyPbb0xa6xHTDSml+rHO13uVUl1M7tj8KfAdpVQzkMBKsf/DYn+nEnsSa2qRnyulvo01Ju1W+98TlGHfK/ZE1Z/Eqpg+hjXg/Zta6zvs59qxxkGhtf4t1gXxm8B2rEr3xWXq5pNvmZuBi7AuTq9hXczfrrUu192YvMptW4QVRCRLW8Ss8t3ez2Ml2LgGK7X+x4HrtNaPlbzE+Zf5FawT4bex0usvAy7U48weXyYvYB2DaK0TWIOzF2J1I30PcKXW+kDZSpfbULlnmPRyvxPrZtmjWPtP6t895SlaTun7SBNWQPlmrOPx37DG2rxUvuJNyljHtBj2Hqw7ys9hjXn5gNZ6E4DWehBrbqWzscbbnoOVct5nP78J63z4ZaweKAPAe0v9BYrsHfbP7zDyWG7HqvzlPK/aP68C/gWr5WMR8La01Oh/wkqS9VOsc8WLjMzUN+Npa9qiy7BaF57DuhH+A+C/x7suyfbLbQqOzVux5kb8i/3vduA/S1X+UrC7N16MlWn7JaxGgf8FvlOufc8wTXPKvqAQQgghhBBCiIkrdguaEEIIIYQQQog8SYAmhBBCCCGEENOEBGhCCCGEEEIIMU1IgCaEEEIIIYQQ04QEaEIIIYQQQggxTUiAJoQQQgghhBDThARoQgghhBBCCDFNSIAmhBBCCCGEENOEBGhCCCGEEEIIMU1IgCaEEEIIIYQQ04QEaEJMAaWUUe4yCCGEEKUg1zwhiksCNHHYUkqZSqlPT8H7fBW4cQqKJIQQQhSFXPOEmDkkQBNi8r4BVJW7EEIIIUQJyDVPiCKTAE0IIYQQQgghpgnDNM1yl0GIslBKmcB/AMcAFwMdwA+11j9IW8cFfBW4HlgIvAp8Tmu9Ie09Ug5qrVfaffM/CnwAOAqIAZuBT2qttxf7ewkhhBCZ5JonxMwhLWjicPdpYBC4AvgL8F9KqfenPf8L4FPAD+11dgEPKKXOtp8/y/75I+BK+/dPYV0E/w/rIvgR4Fjg10X7FkIIIcT45JonxAwgLWjisGXfCXxaa70+bdkdwOu01muUUscAO4EbtNb/l7bOBsDQWl+Q9j6f0Vr/p/34h0CX1vrmtNd8Avg+UKe19pfg6wkhhBBD5JonxMzhKncBhCizv2Y8vhe4Wik1D3iDvex+u9tHyv3Ad5RSHq11NPMNtdYfA1BKLcDqSnIMcLn9dAUgFyshhBDlINc8IWYACdDE4a4z43G3/XMpMM/+vTXHa+cDbZkL7buQvwDOBYLAVqwuJQAyd4wQQohykWueEDOABGjicNeY8XiR/bMXGABM4BysQc+ZejIXKKUcWHcke4ETgB1a66RS6kasvvlCCCFEucg1T4gZQAI0cbi7BPhJ2uO3A3u01m1KqY1Yd//qtNYPp1ZQSn0e60L0L/aiZNrrFwBrge9orV/N+ByQu4lCCCHKR655QswAEqCJw92blFL/CTyAlZHqrcC1AFrrV5RSfwF+r5T6Otbg6TcAXwZu0VqnLlJe4Fyl1NNa6+eUUs3Ax5VSnUACeC9wmb1udWm+lhBCCDGKXPOEmAEkzb443H0J687gP7C6Y1yntb4j7fnrgF8BXwAeBN4FfB74Yto6XwfeiJWK2AVchTUo+k77tdXAm+x1z0IIIYQoD7nmCTEDSJp9IYQQQgghhJgmpAVNCCGEEEIIIaYJCdCEEEIIIYQQYpqQAE0IIYQQQgghpgkJ0IQQQgghhBBimpAATQghhBBCCCGmCZkHTYgCKKVuAD4LLAdeAT6ptd5U3lIJIYQQU0+ueUKUh7SgCZEnpdR7gP8Ffg+8HWuyzoeUUqvKWjAhhBBiisk1T4jykXnQhMiDUsoA9gMPaK0/ZC9zAxq4T2v90XKWTwghhJgqcs0TorykBU2I/KwFjgTuSS3QWseAfwCXlKtQQgghRBHINU+IMpIATYj8HG3/3JuxvAlYo5Rylrg8QgghRLHINU+IMpIATYj81Ns/fRnLfVjHUU1piyOEEEIUjVzzhCgjCdCEyI9h/8wctJlanixhWYQQQohikmueEGUkAZoQ+Rmwf9ZlLK/FulAFSlscIYQQomjkmidEGUmAJkR+9tg/V2csXw1orbWkQxVCCDFbyDVPiDKSAE2I/OwBWoArUgvslMOXAhvKVSghhBCiCOSaJ0QZyTxoQuRJKXUj8GPgO8AzwIeBc4GTtdZN5SybEEIIMZXkmidE+UiAJkQBlFKfAj4GzAdeAT6ltd5U3lIJIYQQU0+ueUKUhwRoQgghhBBCCDFNyBg0IYQQQgghhJgmJEATQgghhBBCiGlCAjQhhBBCCCGEmCYkQBNCCCGEEEKIacI11pPd3b4pzSBSW1uB3x+ZyrecFmbr94LZ+93ke808s/W7zbbvtWBBnVHuMkxUKBQ1Z9PfopRm235carL9Jke238TJtpucYl3zxgzQpvzDXM5SflzJzNbvBbP3u8n3mnlm63ebrd9rqiilKoAtwMe11o/mWOdC4LvAOqAVuEVr/cu053cDR2W87BSt9SvpC+RvMXGy7SZHtt/kyPabONl205N0cRRCCDEtKaUqgduB48ZY5yjgPuBvwMnAN4H/UUpdbj9fAawGzgGWpP17taiFF0IIISaopC1oQhzOgtEEg+EY4VgSl9OgxuOkocqNYczYHmFCFI1S6ljgj8B4B8g1wCta62/bj/cqpc4HrgPuBRRgAi9orWPFKq8QogDJBBgGGNJOMBFJ02QwFKeh2l3uoogikQBNiCLwR+K82Ozl5dYBdnT4ONAXwhsaXTesdjtZOa+ak5fVc+bKRk4/ohGXQwI2IYDzgIeBrwOBMda7E3ggY5kJVNq/Hws0SXAmxPRRsfdeTE8d0ZUXDi9MxMApAUc+dnX6aekPcc7qudRWSFV+NpK/qhBTwTQJ9x7ktV3b2bO/ifbefuIYhB01rG5YwbpVRzF37jIaqtxUuZ3Ekkl8kQSt3hB7ugP8+ZU2/rillYYqN5cft4hrT13GwrqKcn8rIcpGa/2z1O9KqbHW253+WCm1CLgW+Ia96FggoZR6ADgF0MBntdbPTXWZhRD5M6K+tN/9eA48SmzhSSQbVpWxVDODLxwHIJZIlrkkolgkQBNigoywF0/TAwR3PkhN14ssSA6wArgEIP0moM/6F+9dRWzZ2UTWvIXYsrPB6RlaJRJPsvlAH/fv6OIPWw5x+0utvP2kJXzgzCOlC4MQeVJK1QB/BdqA/7UXrwMagc9hJRC5AXhMKXWc1vpA+uudToOGhurSFbgImvuCLKqvpMJV2q5jTqdjxm+7cjrctp9RY92ArEp950GvtcwxgDmB7XC4bb/auiBRh0H9nGoaajzjv2AMh9u2mykkQBOiQK6ubbi3/ZrKPXfjSkbwm3N5zDiZ5OJTWbnmeI44YjW4a8BMYkQHcfoO4ezbjbvjJSr2/J2qHX8g6aknuvpiQse/h/iiU6hwOTh/7XzOXzuf1oEQv3m+hbteaeMfOzr56PrVXHHCYhmrJsQYlFJzsJKFrAbO1VoH7afeDVRprQft9W7EShjyHqyEIkMSCROvN8hMFYwm2LSvl8ZqN68/spF40sQ0TdxOK1jrD0Zp6g1y6vI5U34+aWiontHbrtwOt+1XEbDSukfs7+zwh3AHIiSNMLEJbIfDbfv5/RGCwRiDA0Gcsfik3utw23ZTbcGCuqK8rwRoQuTJ3fYclc9+h8rOFwlRwZ3xc3h2zqWcctp63nzMQirdVqrazA4HifnHwqo3EwKIh/G0PE1F0wN49t1P5a67iC06hdCJ7yey5lJwulk2p4ovXnQ01566jFs37OXbj+zhEd3NN//pGOZP8k6ZELORUmo+1ni1RcAbtNb7Us/ZY89iaY9NpdQuYFnJC1pkSdOaujRqd3vauK+XSDzJxesWArCtbZBwLEk4nqTKXZrU2uFYAn80IeeuNO7mJzDiYaKrLynJ5wWjCTp8YVbPqynodUbYi+mqBFfl+CtPFXNKp9+d9Wb01jKTEAuBp7D9slSMUC9m5VwrmU0ZSPocIcbh7NtN7b3X0/C3tzPYuY9vxP6FGxf8gflv/xFffe/VvPWEJUPB2bhclURXXYTvwu/Td/2L+M77FkbYS/0jH2buH8+ncsft1kBpYPW8Gv7nnSfyhTetZVvbIO/5/UtsbR0o4jcVYuZRSnmwWs7mA+u11jrj+eeVUp9Le+wATgR2TfazewNRHt7VNW3HgUTi5S/XM/v72NLsLXcxCmKa5lCwWwyOsBcjHi7a+2d6scXLnq5AwfuDp/kJPM2PF6lU4nDn6tpKxYFHIBEtd1EsyQRGZBAAR6ADT8vTOL1NZSuOBGhC5BILUfn0t2i4/U3Em5/hltjVfHLBLznnnZ/nlqvP5JRJdhMyPbWET3wf/dc9ycA//YpkZSN1j3+GuX9YT+WOP0EygcMwuOqkpfzq3SdT4XLwb3du486XWzHlLqM4jCmlFiilau2HnwBOA94HBJRSi+1/c+3n7wM+o5T6J2VlG/kpMBe4bbLleLHZi2nCYHhyXYxms3hicueqR3Q3Lx8q7Y2pnZ1+HtnVPen3SZomLf2hsp+vk0nr8ydSDiMemeriiDzFkybxHDd/puWAB9PE4W/Pe3VHoMv6JTk9zp/ujhfxHHwMknGMmNXlMz2RTalJgCZEFq6Wp6n63Rup2/Yz7oifz8fm38Yp7/ga/3X16zlp2Zyp/TDDQXTVRXjfcR8Dl/6GZNVc6h7/NI13vBnPgUfBNDlqQS2/ve5UzlrZyK2P7ePfH95NPClBmjhsvQB82v79nVjd9R8F2tP+3WM/fzPwP1hJQ14BjgYu1FpPWa1/Oh+JRjJudamLzMzW92TSpMs3HCSYpjnhlkEj6svrbn1Lf2hC759pf2+QHR0+WgdK11qWzUwdvpw0TQayTE9zOIjEk2zQ3WzY3VPuouQt1rUL49BmHP62gl7n7niJit1/L1KpsssW+Bohe1ubibSF5Tt4ZAyaEOliQeKPfoUFTXewP7mIr1Z/iwsuvIL/WNlY/M82DKIrLyR65AV4mu6nZtN3mfOP64kuPYPA2V+mbtEpfO+K4/j5swf55eZm+oMxvn3Zuvy7VwoxQ2mtjYzHK9N+f904r00AX7P/HXYqY304wl5cPTuAo8pdnEnb0x1gf2+QNxw1P+9MlakuqLUHNmC6a4iuumjoOd3pZ3ljJTWeqa8OpT53sq2I2fT4IzRUuXE589kG1uEznW4khGIJegJRFpim1RMlS0V4d5efg30zYK6veAgjmcD01I6/bh4GwzE27e/Pa93p1Jlm64EO6sM+1i0u7IaEIzR2EJo0rZs0i+snNhYyGk/iSTtXdPkivHxogDOObMzIkj297mRIC5oQtlj7Nsxfv4klTXfwGy7n0XPu4jPvu54zShGcpTMMomsupf9dj+FbfzOu/r00/vly6h66EZe/nQ+es5LPXbiWjU19fOQv24fmQxFClMf0uqznMFObUTJ0+60WsPRxf46Bg2O2jD22u4fH7JYIIzY853kgGudAX3DMLpRGZMB6/2kkHEuwpWWAbe2D467ran+RuqBV/ulUmd/fG6Q/EBuzNTTVdTg6Tcd4plQ0PWT1dpkCoVgi7+AsxTHYDMnE+CuWgFmEP9WergBbWwfpCRQ+Vu1Ab5DH9/QQjA5vn76g9T7ejNbZWMK0zivT5ECRAE0I06R9ww+Z/9e3YkYG+emyWzn3/f/NFaeuxuUoY6XG6SZ8wnvp++dnCLzuY1Tsf4i5fzyf6hf/m3ccP5ebL1vHq+0+/vWOrRM6cQkhpka2y3kiaY7shhwPQyLLeJ5YaMLdD3sDUZozuuPNHdxBbeDA0GMjS2UjMZnu0YnSdjkzzMTIWp99Sk59LSPsxd35Mq7OVyb8GWPVxzwHH8fd+XL2J6MBXB1bJvy5E5XarwKR8SvlTt8hGgd2AGBm2VP9kThbWryT2ycmYHpUgaefaJ7dd1P3W6IDnbg7XsLVvb2IpcqPWaRbVaGYtZ/nGo83lm57OofUe2RyDB7CiPoBeK3Dx84Of/5vnoxjhAsLpgshAZo4rPkG+2j59bs5cdetvOA8lZ1vuY93XPEu6iunz+TQpqeW4Bmfoe/dTxA94g3UPHcLc2+/kEs9r/CDK47jkDfEjXduo1eCNCGmjaf39bJBDyeaqGh6kIp9D4xar2L/Q3gOTixT3ovNXnZ2jBzEXh9sZo53Z5a1DcIxq4KzszP7wHeHvw0jPEbGxViIin3/wNm/d0LlnYgjOx9hSd+mUcuHgg17vIiRmPg4r1zBwnhJNSoOPIJzsKXgMTdTJWejqGni7H7NSmE+cvEoOzp89PijPL2vt+DPT04i22UiZ7fP0ctHfISZxNm/r+BWjv29QZ470E/HYHnHA061vZ3W8Wpku/kz08SmZuxnIazEIJnn3/z2LXfHFjzNT059oWwSoInD1qvbn8f5u0s4MfAMjy69iVUfuIvj1qwsd7FyStavYPAtv8D71tsxnR7m3P8+LnrtE/zvW+bRPhjmxru20eufBSdpIWaB6ZDi3mJVNhz+djwxqzKXq+XF3fY8nuYnRiyLxpM8d7CfcCyBEU9NKjx2prZoPJm163XF7r9bgUMOSdNkMDy6ha4iNhxQlqtPg2maw92k0loRfeEYZsyq9BuRARyDLdbvUR+OuNWdMpJIZm2h6g9GR91YcwQ6CwqAnf37MEIjgysj3I+rfw/ujNY9wwASUSp2/x2Hr3V4GRPbXx/V3Wxs6iv4dbn4o+N313f2aVzd23EM5uh2GguN6u4XjiXY3eXHG4qxtXX8bqGZHt7VVfJMokCeQWja8X1gQ3G658XDVlBcAH8knndrYErF/ocKWn/K2Dd5hloA89yExWw9AwnQxGEoHEvw19/+kDOefBd1BHn1/F9x0pVfwO2aGck2YivOo/+ah/Gf/RU8rc+y/smruOvUXbQOhHjPr16gPygtaUKU0kSDhv5glHC8dGNHlvZuLvg1rQNhvMEYB/vS726PXYN5dn8fz+4fWXFPtUa5+vfkfN3ODh+b9vfn7I408v3GXWVcRra/nJlkRfdjVIc7RizWXX6e3tdLxN9Hxb5/4Bg8RCAaZ39viAN9VuDqOfj4UFDkObCBeW1PANY4mOcOjq7MPX/Qy4upOeISMRwDB3G3bsLs2EbTvp1ZW6fcLU9Rdeipoceu7u14Wp7O/BIZPy3PHeinr8cKrl292acB7PJF8u6NYZoQik7N/tvtj9LUHaTTN/ZnG3ZKdiPHmKuK/Q/hbh3Z4jqZ6XDA+p7pmUSHTHD+LiPQhSPQMe56KzsfYnHfc/m/b9RH+t/cF45PONtzS38If8Ta1u6OLbi6t+ffFds0eaapj437+4YSckxGZ7Ztn8bZv3fE3zyeNEedQ5r7Q/TYN7ANDKrDHVQOZN4IsfaTYGx6jOuXAE0cVg52e9nyqxu5puUbdFYfTei6h1l2woXlLlbhnG5Cp/wbfdc+QnzB8Zyw7etsXPzfhPoOcdOft+MNHp6piYWYNDOJEcwyB5aZzDkQf6KxwvMHvezuDIy/Yoa8upaZw5X0voybNoWU1zCTzPHvs+8yZwwAyyFba0ywLXfLWYrXbnUba+LvXHXtNm84a+tboYywF2ciylzfrqHt1D4YHgpQzbBV2XQEO4cqv6muoynZWhjHSub00iEvvXs2DY11OzQQJtb8QtZAyRHqwxEZ+8595txNqe8RS5i07ts6tI679VmWto7sdvvyoQEraCxGtgdbqpUxXczeZ0LjtqKldoDc+2BmVsBitLo6fK1U7Lt/Qq0ontZncbdmv1mS+a0qo/m/f9I0h26EJJImz+7vY2tr/sGRq+Mlq/XWTLKrrY9NB+zPTtrH1Xj7RMbBGYsnefnQQPbgdgrF2rZipAW8W5q9PLV3ZKtyly/ClpbhbbHQ+wrV3uw3KZ5t6qM/r5sUxW3PlwBNHDaefnk7zjuu4orYPzh41PXUvvcePA1Ly12sSUnOWcnA2+7At/5m5ntfYUPNl1jRv4mb/rxtVIYiIcTYwuEw0bZteA49M6ri5W55moq99w4vSMSoGKPy5I77WdnxIA7v/ikr32A4hmmaPLKre7jylIMj2DX0+0BoZKW3kACtwt9Mo38P1YN70ypghYeknv6MylAiCvHQmAmOxmrJMYF9PQEe392DiUnbYJjN42yTQI5BhLgvAAAgAElEQVTWnvR409My3Dr1yK5uIvEE3TladYZfN3J7uNvya/WoDnewsuNBegYC7GntIRRL2FnksqyciI5K0JK1BRBwj5UwJe0mw9BEwVk4/GktPNFAwZNc9waiQy0WmeLdWSrG9ldxRIe7II7Z8JVZHtMcFUAYkUGIhbLezAhGEzzT1FdwN7wUh30TZ8wxm1lk69Y6VV5t8/GS3VqV+nsVUg9wDjbj6n4Vd9tzHNG1YWiC8/Tnx2R/Zqor9GQZoT6Ijn0DKxwOsacrQOvA8L7mDcWYE2jCnXYsj8fhb8OVNo41da5wevdjhPtx9u7iQG+Ah3Z2DScrKXJmXAnQxKwXSyT58913sP6ZazjGaKF5/Y9YevX3wTl9EoFMiuEgfMJ76X/n/bjrF3Ob67tc5b2Nj971ypTcURbicLHr8d/z2kGr9SNz0L0jM2Br28ySvudy3lWeP2C1VLi7tk66XEa4n8FAgE37+9nXY1V+/GO0yCTsitK8ge0s9G4d0XpTFRlZKT/kDY2sdGR+dlqXMkegc8Rzzt5dOAYPWZkM23N/z8ro6AQUFU0PEXrtPrY0e2nuD+Hs1TR4R2aiG+r+l16e1C8mQ10Lk3bRl3Y/hdPblLUMS3ufYV93gJ5AdKhFMZ/6VT7193rva3j2P5L1OUcyxgLvyziSoyvlcwJWWd0JqxK6pyvArq7hLHKvtvvY32t9x4p99+PJGKMTTyaJ2F1k3S0bR+2LCdOkwxchPeIzxmkFWdT/Ag3+PSNes+/APh7elaVVeQwvNnvZ0jJAe5akHOnxUurX1N/CmYhYQWRGOWOJJKZppmUKHBk8GK3PU7HHmps+Nd7Pc/AxKvY/xJNprSkV0X4cgQ4O9AXxR+L29hmbMxG2t8mIbwEUfnw/f9BLq9faJs7enaMnaE5EafDtHreVOldw3uOffPCXOs4ro7124g7rs5zj3Gxy2vt4tsA/mUzi7NlhBV158rQ8RcWB7McVgKtrG5599wPWlBnpGn27cYzzWelb2N32fO5yND+Jq3cX7W0HAGtMaSlIgCZmtf5AmA2//Qr/2vJpkpWN+K+5j6oTrix3sYoiMfco4u97hNCx7+KDzrv58sBX+Pxdm4f6kQshxmYCRuqyPc74Eod959zAtO76J4ePM9M0cYxRET7kDbGxKf+seZ7mJ60KODAwxk2XVMU0kbAqsnWh1lHr1AVbhip/W1sHeK3d6g7Xvid3BSUlNW7JiEdw9mpcvbtwd7yIc7AFo88azxG2x354YgNDyS4W970wur5pJojamfxCsQSu3p3UBkd3fcuUrWKaemt3PIira1vW13nsRCNt3jAvHPRaQcBQED66MuxKhPHEhrtEueK57+QbjJxfLV1d8CA14U6W9j5rZSAcEUBa3yV9KoT03SYaT7I7LWBLBcup1cOxJNruIusI9Qx1bQxEE7QNhGkbCNM1GCEWTw/QRl4PMrdmVaSXBv/IhBDhjp3Uhg7R44/w/MH+cVvT0ru4bjvkHeo2Z5omu7v8JLJUcA27OmoCFXvvHZFZL5ZI8tjuHnZ3B8Aw6PRFRs+NNnAIgC5/hNfafTmTnizpew536+ahgHBnh2/cLngrup+gwb9v3O6M8USSQDSedzp4V68efmDvG7UHHqQh0ER1JHfr5lhS569x2ztN05rfb4y/5eK+F/A0P5Z3S1HUXQ/ASwPVOBIRVnQ/hjtun1/aD+Hq242nZeRNlFAsMaoLdi47Ony0tFrniHgiSduB14ZuRmWTbVqJvGX5yvF4Ak/MiznUki0taEJMSFNrGx2/eRf/EvwNzYsuwnjPwzBflbtYxeWuxv/GWxm84Huc5dzFzd7P8K07N4y6uySEyM4Tt7pYuTteGntFO/NXbaiV2pbHcbcPBziBSAx3WoXec2DDiJe+1u4bkUnRxBw1mP+19kF2pKXQT4bHH0uyuzu/OXxSn9QxOFwxrRjMcnc8HsYd7h4qY4oRD+HqHZ3Kvy8YHZpIemnvJlzdr+ZZEmucVziau2IbiSfZ0eEjOVQBHbm9KqOFdTVzH3qamv0P2sVIWt2YMlrfqtJa/pb3WIk4Cq+SWa9wJcI4+3aP6HpaEctvfFCh5+993QF6/NGsdW9XlnTsjT6Nkcwd+LsSEeYPvMrWtkH6g7Hcc6ZFA5CM81rHcDfFOcEDVre51k1Et/+Z5q5emr3DCWeq99494i1Sf9dUsDkn0IQxYHWt6/RF8IVjdA5G2NuVfaqIVJe+yBjJd4IZXV0zk1gYUT+u9hdGt45n2aAH+4McGrBaxDYd6Gfjvj427O4ZtV6ussaHWvsez7i5MPxZ0XjS7uJqHVvhWGLMwCRT3NdD8+Y7ceh7hlLZOwabcXe+jHOMpD0ARiKGaVpJXIaOuXiI1pYmHtrZNdR99KGdXVRFrHNFIJKgOtKFMxGl3p6TMf14TX1PI251TzzUn9bKGg/jaXoga0KSCl8zwd2P49j3IFtaBugPxLJ2P3Ym7PGiJrijo98nVZbBMbp/ZtvHDTPJ0t7N7H35MXuBBGhCFOyVVzax+G+Xc7b5EntO/AK17/gleGrKXaySiay7hsG3/oGVHi/fHfgUP7rzb6MuSkKILAoca1MdsboD7Ws+yEM7u8A06W0ZDl4ODYQxoj4GwjG2tQ5mbdFu7guNmDMN4JA3TEvaJNSpSsziQ/czb3B04BOOJegL5NelOZ4w88r26jm0EXc4/5a+Fw56RwSVAO5Uivy07Zo0TVr6Q0MtaEDOcV4pe7r9tPSHGAyN3n6ZrSXtB3cMdQ3MxZE2dqihfxtd2x/C6Ng6Ivubdfc/oytdbHRgMBCKj3hdXzCataUzvdvstjzSvc/17QTT5LmD3lEBaVWke6hCPFlzAvtpCGSkUc9yHCRj0axdNVMqDjyCu3XzUJdTGG71cwS7rdax2GBBCUgafbvxNW0eHt9kt1RWDe7F1b0dZ58VYISi8RHdKcc6jPd2B2juyz7nlufAo3gOPIrT1zpq+oJ0zgErzf9AME6ffVMi8xrr9DbhTG8lSxNLJGnuC3GwLwjJxKjELuke39NDxb77qbC78z25tzevRGDuuB9HMkZP0/OEQmF6/UGcfnt6BTsgNxIR8HUMdbU07f/SdfkjtA+E6fFbr/E0P0WwaSPORHhonFpltHcooUlNpHOou2OhQYwj0IkRj+Dq3UXCHFkWlx14HerqG3Ns3Yru4bnJ5nc/Q2W015rwHhgIDd9gyFUnciTjtPZk6V5tpnoHFD5Vw0RIgCZmne0P/4JzN15HjSPGobf8kYbzbir6nY7pKLb8HPzvvIfqqiq+6f08/3fn7UPdj4QQOaSdK9Kz8QWi8RyZEw3294YIRuI0+jQu/XfCh4bHpaQqb/12hSpbgJaZxGMsJiZ1wUPMH9g6NK8ZQJ+3j5UdD+b9Ps8ftF7rSEaz1mYd3v0Y0fxa5NoGwrzU3M/8ga0jEqdE4kmW9j1rl3tYXyBKfzA2tG0yP39bS9+oLqY1A6Pv9Kf+Uvu6R3YvHGx6kcFdG0aP70m9l931s9cOUj3BDvr8Udp9EfZ0Db9XbaidWMaEyh6726ZzsGXEXfbetArzof4wB3tDWcYDGWRvg8seTdQHDuKJDxCLJ9ndNfI7LurfwqJ+K6V/LN9U6mNELYaZHLXNewJR2gaGg56F3i0c0TV2l7fRGRTTum+aAOao65BpmkMto5nbGyCRHB6v54hblXQjaU1Y7eqxsoPu7vLT7Ysy2eFBI/b5jO3lCPdZiVrMJN5QbGgMZMoc/17mDQyPo3R1bcPVu9MK9EzTmrNuaM4tSyCSGJl8aDLd8rAmdo92WV2Rl/VsZFHv5qEunOFYeiZa628YTyQxmp8Zev32Vh/bW4eDxYP9waEbRQnThEQUw/4bNPo1zj6NZ9/91thBmzseoDFjzJ6R5XtlC7JSmUwd/nZea/PR3BdiWY+V7KM+ZLWk+nIN20jEcKa17Kc+cXHfCxzZ+Qg1oTbrO4xTH1zW+wwNgabRUxSUuB4pAZqYNZLxMAduv4kL9nyD/RXHEL7uYWpWn1PuYpVVYu7RRK69j1jNUr7o/Sq33fkHCdKEGIOJQcK0uxzad/+D0QT7uoNDg/sdAwdGrJ9SF8o9hmo4GYKByzn6Qn9E16N4mh6ygoocLQyprjtgBQ9D85rFw7gyWroyk5pkUxNq44iux1jZOTL5hBH1jUp+EBojW0YqEKkNtbPQO9w1NJ40R4yvysWV0T2xsf2JodaClKpQK8u7nxhK1vD8QS+xhEl9MPuExam7+dmSEiwY2E7rQJiuwZEBSWbWOiBnSwswtD+MaZKVOsM0MZJxIrHc239nu8/uBpmR5jwj2DHI8h5pL0nf5iYmbd7wiCAzVzfSgVCM3V1+YokksUQyYw6q4TJYXWpHlmlXh5+n9vWyu9MKjIJpXX/TxxumWqpz7k/24lieWRnnBJqGxkcBBL2dDIZG7g8G5ojxZK7uV3F3vABY+0V6a+7+viCN/r0jxn36InFiiSSelqdx+A7h6t5Ogz//ScjTtQ6E2d3tZ9Pe4eyaFdH+oRbN9Jamg3uGg0R33D800Xs4luTZpm5irVsw4iFiiSQvHOznpeb+EYF4uoFgPOPvnpGxtHcXRh7zwdV2j+wy7o/ERxxbuVqzBkJx3HZWSEdqnK8J9YHRXbIr9v0jY1zfyOerolaLc7Uvv8y6O9p9hGKJoW7bmfK9gTVREqCJWSHpbSH468s4ve9uHmu8lsb33U3FnMXlLta0kKxZRPyavxKuXcHn+r/G7+76Xc4B1EIIg73dAXa0D1feUvNyheIJjEBX1jTmhmkOVyDG8MqhASrdzlHLHcn40J3pXPOtreh+clSlY2XHg0R33kfXgZEZELsOjjf+CypjObKcZQkQvcFY1gmkxxoflT6fWXqx039v8O+hofMZ0mUbJ2UtD1sJLMykPW4qTk3GhNKZ5TGig1m7AvZmqXSNF0sZZmJonVzjsKbi3DpyfJFJTbht3Nfs6x7dpTO9G+vy7idY3Dd+Iph8bGsboCOtO+Hm/X3s7wmws8PPzg7/iMp2estJNJ60As6MTZc5j1x29oY3UslErP8SSTPreKXxbgs0+nazrOcZDDNBZaSHlpcfYuero7fP5owJxh2Brqwtkb60YM0d9+H0NrG/JziU5CU1QbvDTtKSbVdLJM2h8WyplraUXn+UcDTJ3JaHafDvwR3zsaTvOeYP7gCsbpspyWjImmIgQzSepDLah79jL87+vSO6GOef/dEY+n/CUTH8nRNjByuprq5d/gjbWgfZ2jayfHu7C5sPMnVMZPZqGGsOxZRa746h301MmnqDOQPEPV0B2gfCJJImjb7h4K/QaScmQgI0MfPtf4Lq2y9mfvggf139HY5/1624XLMkhf4UMasXkLjmbwRqV/Kpvq/z+7t+P+H5X4SYzUzDGGqtSJom0XiSbnveov/P3nnHx1GdC/uZme19pVW3iuvYFjYGTLexMQZCqCmUhHsTchNSyQ0kJDe93ZDkQnon7UtPIOGGmuQSOoRAEjrGDMW9SZZk1e278/0xu6sts6tVl+zz/H4G7cyZmXPOzM6+73lbKg0PvXSgwDVHL5Lqk+nS71W5otKeyJ5yvcj99fArY2dz6xqMYU0WWnp6BsZfADvLnv4oz+4dLFFC4ql0zlUzdx2zYsqZ+Jb8FfLdhyJEMskN0nlzZChc5v0oF+/kjew2j5sCwnmJRqLJFA+/0ptzBRyLSlYqgNDAsznBOj/RRT6DFcofoKdMi1gXU2hF0k3dw8x44OWeAhev/CLHllS0ICmJjs6zewcLFIt8rAf+Vb5/3dt5flc3HQf+gie8m46u/yuwwip59aTM3SonLtzmF+l+bu8QW/YPob9677iSZuRTO/h8LrYpMFxcokEvSOaTv70S9vgh4t0vAeRcLosVpi4ThahrKEYs8/zW5blKFrsuB4ZfpaV3dFGjezhGpCjBjmOXkQWzWBGstIhUNvlLEWl3PQD+TAIQo0/m5S1AwpJXF+1Apl5ZpTIh4yH/tu8LS2w9MKoo6sBItLLH0MHhOMPRJLsHylvKwUjspOQl0rn7xYMk0npZy+NUIBQ0wfxFT2N97OvU/unf2Zv0c8dxv2D9Of+OdATGm1WD7qxBv+x/GXZ38P7ez/H9398i3B0FgiLyFZ29LzzCYy/vzsU4Ge5TOrv6IrkV1/zipkAu7Xk+z+8bKhGErckhQgPmVi45eii34ly/+y8FMRfmAuPYmK4slxFq92aElfxU5hI6O3sj7D4UKVDSBsKF41LSCdq6C7NWglGr7OXuEbbsG6Lr2epi5WqGRgsaK+lRq1q2lpeZy97+PIEpHE8RGtxS1bWy7SvhSIwqB5Eqky4VxN717GfYxOIooWOPm1sz/cOvUju4NXOuygJ0Kq3zwrP/HLNPQ9FESX03Z7yXaIXMh/nUDTyHJWUI3R4T617rwQeo63+yoExBlpqhrSXfmdrBLaZWQkd4VJm1JwbKzrlZrTV0I+4zkUpT1/9kLoatGE9k/2hCCyiMOyrz/eg6WHnRRJeUsgWWs2UO+kwUtIkumR4YGLuWWxZbchjQ2XJgiP5IAl94tPj0lv3lE5XkMxhLF8T6jWW1qht4qqRNpayhZhQv1oxWw9Nzn3sOFT5vxQtjlpThlpo/9+OZu2K27h+akrpz5RAKmmBeIkX7cd1xBYEnvsodqZP514bfcubJJ892t+Y8uiOIfsnNpJx1XNv7Sb76+7tEdkeBoAzD0SQNBx7MpYqG0RiYrEuOfZwZvaR0Ekekm5aev5VtY937d+oGRmPAtvdUzkhYTe737MpyvqIzFi93j9A9XNq+JHh+nNiLMiGWF3LyUvvnuX3WZF2NxrCaVLRmTYTM9Xb0hUkWxXeNZJRok+Tcub929UXYdjBcYoVs7CuvVDnzFLe9/VH6Iwl2l7He+cPlrBiFbO+NEI4XLxiMFCRbyU8UUYl8K10+7mg3zb1/L9mumAjm3vBuPJFSy2Lw0FMFnxv7Hs/NZr7FqKQmGnBgyMiC+kpPGHe0m+DQS2XHkFU2gYKi7qldj+KOlCqOWw+MNTfmNs9YMm06zix9JtboiZJVts10p4FoklRKN3XzHYvBaJytXYXujLvHiMW0JwxFJt/9V6mQDXQkXlpAvK37voLP2bqGlTyB9hdZt+yJAbQu83sXq1DiY7YQCppg3mE5+Dz+m8/BtvshPpt8G9Gzv8PZqztmu1vzBt1dT+Lim7HZnXy07xN84ea/imLWAkEFgsOjsQd1A+aFkKuldmgLnq6xY4FcsYPjyspYLdUolOHYqBCdMsmqV02cx2QxtYqME7OU/JMhGz9kmuq/ijiqrBUgv3zCeOgbSbCrL1K2XlxxPF4ldvaW9mE8t1XWZ35hr5wyaDYfOzPutflJQ9q67xnzGsm04fr53P5BXuoaKShpEUsaSVA80dIC8MUUWztfOjiM1jVGUokpDGvqr/DsT0YZeXxHYaKYHX3hqt0V88efrSuYT3bh4tWDYboHq1tIqvTMFss1kq7TcGhsC/NYKOkErujYrsqTRShognmFfevN+G+5gP7hCG9KfJrV513NJrV+trs170j72oi+/ncErSk+3v9JPnbTwwxUqCsiEAimhvwC1sUcGIpNSPkJj9Pt0R4/RMeBv2BNlfZFR6epp9Ty4RsZzZbYMxxnOJ5kZBqs79m4vOK6aGaxfdXGZlWiZmhr1ZkWx8pI2R9JFGT9g0KLpZn1aL5SbVzfRMgq52YJVyYTwVAu/sqRV64ia3UxS6SqdRlJULzhcrGjoxRbWCsVYTeL95ws05bCQtfJtwqP5XKtV2PezzDRhQswn1+zV6mzjCvxeHELBU0gyJBO4n7ks/ju+yDPSct5bew6LjvvAtYvrp3tns1bUrXLCV/wC9qUQ3xs4LNcc/Pj9FVRvFYgmElUVbWrqvq8qqqbK7RpV1X1blVVR1RV3aqq6jlF+09XVfVZVVXDqqo+oKrqkvH0oZpU8dVSyYLVPRhj64FhU5etqaSp73HAPG36tjEKPOfaHQyX1B+bCvLj8qQ8Kbk41g0M17jJkq94VkOlmnW7+kprn7liYyd5mSqm8jmdSoai5hlAy3FwKE5a16tOmW/KOOai0qKJPI6i2lkc8d6CBDmVMp1m2TNGkorxMhNZBo9spj/XgVDQBHMeKTaA/8634nrmx9zpOJ+LRz7MteeewIYlodnu2rwn2bSW4dd8n9Xydj48+CXe+7sn6BqaeNCsQDCVqKrqAH4LdFZoIwG3Ab3A8cDPgVtUVV2Y2d8K3A78ClgLHABuU1V1zv7+mRXrnW6ymSnHyno2k7ijXTNyHblMWQMzxpu1zZqsTuE9XPGFd7G9N1JQALwani+TsKLaMgb5cWWzjVkJhGLCUxwDZVbTbypwx6p3oxVMjjn7AyUQACiHXiXwh/Ox7n2UH/o/wH8OvInPvLaTTcvqZrtrhw3xhWcxvPFLnCY/w3+Gv82Vv32KPWWC0AWCmUJV1ZXAY8DiMZqeDqjAOzVNe0HTtC8DjwJvz+y/EnhG07TrNU17AfgPoBXYND09nzyxKrPpTSV6empqeAkEU0IZ/aJS4fB8zGKcZoqJuN5OylpoQiVL72SoHdw6roWT/CyRhxMzsXgkFDTBnMW660ECfzgfOTbA1xqu54tdJ/LRzUs5a7mIOZtqop2XM3LCh7hQeoh3JH7Flb97hld6pt59SSAYB+uBu4Gx0rOeBDylaVr+kvsjecedBDyU3aFpWhh4sorzHnFMNjujQCCYWbfWmaTHJKPrVCPeQaNYZrsDAoEZ9hd/j/e+a0nVLOPrNZ/ju88lefep7bxuddNsd+2wJbz2auSRLt6+5Vf0pYO8+6Y033zDKjobvbPdNcERiKZpN2b/VlW1UtMmoDgXdhewoMr9ggzV1vYSCATlqVQMej5jkqdnynmhylpsRwJCQRPMLXQd55PfxfPYl4kvWM+NDZ/hu3/r5pI1zfzHiW2z3bvDG0li+LTrkMMHuXb7zxiw1PDem3W+9rpOjmsNzHbvBIJyuIDipd0YYK9yfw5JAptj9n8WuyPJWelHT2zi15UkaU7M3XxFzN/kEPM3ccTczU3EHRHMHfQ07kc+i+vZnxJdeiE3NX2Ur969nc3L6vjQpsVIk8mvK6gOWWHwrO8QuP1yPt/1bUZcn+ID/6vzP+ev5NRFNbPdO4HAjCjgL9pmB8J5+4uVMTtGUpECdB3iU13c+AjB5rCIuZsEYv4mh5i/iSPmbm4iYtAEc4N0Cs99H8b17E8JH30lf178WT7/1+0c3xbgc+eoyEI5mzksTgZe+1PSgYV8JX09ZwS6+NBtW7j7xcPTr14w79kLNBZtawT2V7lfIBAIBII5hVDQBLNPOon33mtwvngTI8d/kEc7PsBH79RYVu/hhgtXYrOIx3Sm0R0BBs77Jdg9fCt9HWfUh/nkXS9y23NCphXMOR4D1qiq6s7bti6zPbt/XXaHqqou4Ji8/QKBQCAQzCmE5CuYXdJJvPd8AMdL/8vIiR/huUXv4oO3vUCD1843Xn8Ubpvwwp0t0t5mBs77FXIqxnf16zizTeYLd7/Mb57YM9tdExzhqKpap6qqJ/PxQWAn8DNVVTtVVf0vjMyNP8rs/ylwoqqqn8ik7v8JsAu4d6b7LRAIBAJBNQgFTTB7pFOGcvbybQyf/Am2LXsn/3nLc1gVmW+94ShqXLbZ7uERT6pWZeC1/w/L8F6+o1/HeUscfP2Bbfzw0R3oukiHK5g1/glcC6BpWgq4EKgHngDeArxO07Qdmf07gNcD/w78C2gALtQ0TRT9EggEAsGcRJgnBLODruN56BMZ5exj7F/+Dt5/09OMxFP86LKjafE7Z7uHggzJ5hMYfM0P8f35HXyt9rM4l/83P/r7LkbiKa7esEgkbxFMO5qmSUWfO4o+vwJsqHD8n4E/T0vnBAKBQCCYYoQFTTAruB/7Ms4tvyJ87FX0rXo319z6PPsGonztdZ0srfOMfQLBjBLvOIPBc36EtXcrXwx/mreu9vKbJ/Zy3d0vkxKFJQUCgUAgEAimDKGgCWYc5xPfwfXkd4kc9RYGjv8wH73jBV44MMR1567g2AWi3tZcJd6xmcHX/BBLzwt8sv+TvG9tgNueP8B//58mlDSBQCAQCASCKUIoaIIZxfH8L/E89mWiSy9icP1/8/m7X+bR7Yf42OalbFwamu3uCcYgvvBMQ0nrfZFr9l7NtWud3PVCN1+4+yXSIiZNIBAIBAKBYNIIBU0wY9hfuhXPgx8n1rGZwU1f45sP7eDPW7t5z6kdXLS6aba7J6iS+MIzGTj/l8gj+3nPjvfx0WPS3Lmliy/e/bJQ0gQCgUAgEAgmiVDQBDOCbcc9eO+9mkTziQye/X1++q/9/OaJvVyyppm3ndg6290TjJNEyyn0X/QHpFSCd267is+sGuC25w/wpb8KJU0gEAgEAoFgMggFTTDtWPf+Hd9f3kUy1Mnguf+P3z3bxw/+tpPXrqznQ5sWiyyA85RUXSeH3nAraUeQK179AF9fuoVbnzvA9fe+IlLwCwQCgUAgEEwQoaAJphVL9zP47nobKV87A+f9kttfGuGr97/KxiW1fOpsFVkoZ/OatL+d/jfeTqLpeF63+zp+ueBO/vjMXqGkCQQCgWDWGHHUz3YXBIJJIRQ0wbSh9L2E/45/Q3cEGbjg19y7J80X7n6JE9sDXHfuCiyyUM4OB3RHkIHzf0XkqLewvuc3/Knue/z5mW185b5XhZImEAgmhM0ixBOBQHDkIt6AgmlBHtyF//Y3octW+i/8LY/22PnEnVs5qsnHDRd2ih/fww3FyvCGLzJ02hdQhx/nHv91PPrM03z9gW1CSRMIBOMm4LLOdhcE84B+z+Iye8QCsKAUu3X+yJ7zp6eCeYM8vJ/AbZchJaMMXPBrnhgKcu1tL7Co1sU3XncUTqsy210UTBPRVVcwcP4vaaCXu5WbGygAACAASURBVJ2fYv/Td/Hth7YLJU1wWNHot892F+YkDtvUiRS106SgzScBbbxEbcFpOW+NxzYl5+nxrZyS8+Qz6Gyb8nMKBHOBw/dNJZgVpPBB/LddhhTpY+D8X/H4SBP/ectzNPnsfPuNq/A6LLPdRcE0k2g9jUMX34U12MpPbTdQ89Q3+f4jwpImmB8kLO4x29RNkcA63/C7Kr+/7VPoGWFRpscCsiQ09v2dLeyTUHATFjcJxTWFvRnFrkzuvrbXOgFIyzP5vREWNEEpU5X2YCYW6YSCJpgypOghAre/CWV4HwPn/YK/RTu4+o/P0+x38INLjqbGdWQKNUciaX8H/W+4jdiy13ON9RbWP/UBfvXw87PdLYFgTAZc7VW1O6rZO809mV2KrWErGj20B13IcnVK7GSRigRsr9PCyqbSOT/kWTKu8yomsc9T4XIft3gmfY5mn2PS55gqWoPO3N8hz+SsmaOK+9QrTRI6u+pOH9cxvb4VU96P8aJM0wJENaxoHH1Wx+1KPI5up5SZkfmSSmVlqd+zKPe3dZKLDVmK30/TgVDQBFOCFBvEf8e/ofRvZ+C1P+Xh+BI+dOsWWgNOfnDJamrdQjk74rA6Gd78DQZPu47TlOd54zNv4a77/zrbvRIIxoWZe5eENGUrsXOJUN5YlbwBuh1KTrCZLTu4DKaJpQbGqaCZ4Sjj9qhL0phug1l5Ly1XFnQHXdPtiqcjTeHdkfOmpFgYtVZQaOUKUmXM6s/9na/k5yuD4yUl20Ayv6jLbh5OMVTlIsx0IMuwusXHgkChMt5RW34OugNHT2kf8pWUtry5X2GyAAKQsIy2MVvgKEePb1WBcjRdJJVxPD869HnV6evMFCIUNMHkiY/gv/MtWHpeYPA1P+QvkeV86NYttAedfP/i1QSF5ezIRZKIrXorA6//X7xWuGzLO3nyT9+f7V4JBBUoFEBa/PZpi+0xvXrn62fsWsWUsySNK+PuLCiu47WiBau0Gow4Gsds47IZbp8H/eWF6J0NZ5KWZ8K9f+oUtEoe6TUV5i9VwZqRUhzsqz0JoMAdM+iyTjwpjCQXjDqVpyhP5lFs9Fdvzaz3mY85aguytN7NiiYvQbfRr2aT8zptMj7HRMc/scPMsJb5ng87FuT+3t10TlXnOuRdRsReN2a71S2+km3Ocbv6lvZblg2XbLPvcMQWGvOMY70jZuI1JxQ0waSQ4sP473oLlq4nGTzrO/zq0Ao+dsdWVjZ4+d7Fq0UmLgEA6aZjSfzb3Wx3dnL29us48PurIBWb7W4JBFURdI4K19nVbAlpUjFDgOnq8rL6Ule5cEbQyXdNAkPwny5smVX2lGzFqsikvC1jHhNxt6Ga9H+6saRjuMtYS8wotthMVK2RZWjMCOcppbxAr0tG30IeG4vryruHShIMO5tKtu8NrTNNbpJUnHnPkJ7r03Qy1vmzYy1H3Bqgz6vS4181uq39dLpbSp/l4jH3+laWiU00F5fljMIx4mgclwUz6LIS8lSvoElAzFpqfeoOHIvTqmCVzR3iwlUoMKNXMGdZ/UzEVI5+Qxq81cVexS3egkP9zskvUFgt41OL2oMuDgbWlGxPmNyrEqRR63iWArfvGdDQhIImmDBStB//7W/Cuv9fDG7+Ft/cv5Ib7nuF9Ytr+c4bV+F3CuVMMIrkqSPw1j9yl/dSVnXfSvpX5yMP7Z3tbgkEY9KSFxAetjeiWwzhbUGZVfYBd8eY50wqdgad1blaZV3XiuMn8oXh/Jg4l61QSC4WTg95l5W9Vr3PjtUi48sIVGF7PU0+O2lPMwAWWSYpj85HvsA+EFhVYG2LWb3srT01J4gW96sSepHFqZIS5Y7uB0qTmHQFj6v6elBZ4R1ytQKGApVdeFze4MVpVbBbZVY0Vhb6IrY6mv0O3OOYgyyJMvFtulRo3YCx3QVj1kKLxYHg2nH1ZSy51EwVKd4y6F6YSxhiUSR0ux9dKpQXVrf4SpR9XZJZaTLPLTWevDajz0Cj1069z06tuo6+cWSQDLsXEF1yfsn2XfVnmLa3W2QWrNpMvc9W4G05lttrtbGcp61oLd04QQWh0gJBPvtrTgSMZBi+THK3QVdbgUsklPUuLbGeKWPEfqVkKwGfm+ZAecW4NejMJZyZCUKe8sroTJTxFQqaYEJIkV78t12K5eAW+s66kY9vW8FPH9/NRasa+Z8LVuIQqfQFJlgsVo69/Aa+VfsZnEPbcf3mLKy7H5rtbgkEZZGQCmOUJIn4otcA5ZWGsL0BMFx1iuNgRhzGvrCjsaqYIUO5MtqlfKOKVrFwJ1cIihvMWFmyWXQH3ItM60dJGELtigZPTv7r864gsWAdus0QjJeEXLQ11eeOqaQQDLoWkrB66fGvxltTT1tNadty7qPJxuNYWu/OKYqVCNuN/rQHXbnkCynZWpWL1djomf+Ozq/PYWF1iy+njKr1nhLBNduHLDFbsOD+gWERWBgadfWrJPMNuxfic1oYdLebxoBJeY9SJcuu5C9U6OLWUhezLCGPjZaMwJy1ZjX5HXjtU+OuOeKoZ7g+o0RXeH5HHPUFn7N9yi6ElPPUkSWJRq+9uuyimcvvrtvAQOAo0ya6JLOzfjMH/asLtgecVmo8Dhq9jjEzvOa7jo44mhhytdJdv45Ey8nlj3EEqfeZnzdlr6l4vXzSklyyQJAKVI4R83lGFeViN91qrJLVvON6fSvZXX8GjSs3UhusLdvOrsj4i1xBm/0OGqtIrhNwWZFkqPOaz2PAZcVqkTjkXUbC4mag9tjcPjMX6qn6DlRCKGiCcSMP7iHwxzdiOfQKuzfdyBX/aOCuLV288+R2Pn7m0vHFKwiOOCyKzMUXv53rW77HrrgX/+2X4/znN0FPz3bXBAJzgVXXc0JhNa462Yx+NkVmSchYFc6uDMesfmrc1pzAnx/3lS+sA+xofI2x8p+R6lLeBfT4OtnRcBZ7Q+tz7bxj9Knea0dt8BSUORlwLTRtm8pYyrLuYbqkoLtGFR2rIlPvsbOj0VBS/Q5rwXmz32JFkThtiRHrkZat1B11VokC0lbjpCtwLHJoCXarXJCNTZctOK1KVfFixekx9oTWF8yPGYOuNiK2GnbWb56y36xil7x9taeMusSaXMIqyzja1tIadKI2lFqGhlytOSE45PdS67aRlhRseW5+uiTRUevE77QgZZ6TliLLrss9+lxZi1wE07KN7sAa0yyUzX5HLsFXtn6p7AzisimsajG3GJovFEim5z8YOJZYxqWz3B3QJYmD/mPY0XBWblut24bNV8chj5HsoVhozxxZ5ozm18gqISnFWd4spOvosoXGtqUFm9POGsgpLxLdgTWlNd9M5kWXZHp9nSStPtLuhop9bPQ6CuK1qk1StKPhrJw1LGEZvWfZDItpR2UFT7c4sVlkFoZcqPWeguv2eZfn/i5fMDzjemvS385MUpJskg/dWUu89TSjXyYxm2YZGBVZYkHAOaZF0SJLrGry4awxVyp1dKyyzIB7EXtD64m5mnJjDZh4g01XGZB8hIImGBeWg88RuOUC5JEunj71R7zhAS+v9IzwP+ev4MpT2pEOx9RmginHoshcdeFmfrzkRm5PnYznHzfgu/MKpOih2e6a4AgnbvXT4+ss2CahY3OXWnqyq+H5Qe1RW6DAzQog5LblBGOJjOCbcZ/JxjB1B9Ygd5xm2qec+iHJDLtaCwTIGreVjprimCq9JMlBsRWh2IVw9FqGiuXJJL+oJAlmrVVZJUpnVE4qrp0lAZH2zRyoOT63TZZAl60kLR46apzoFYTUSuJ2fhY3RZJIWtxj1tzq862kq+YEljYGyqa3T0vjc9O3FY05pTgJOwwFZNNS88QEus1L0GXFbbdQLGX2+jrp863k5IVBGjs6iQVVwy0218z4wzPGan5Bv0yUj7CjscQdrzhJSIvfQaPfgWPJRuJtG4i1nY5e9GzYrTKNR5WmvK8kFhzVVN6CBzDkbDVOUNTvQe+S3IntFpl+n6EsJDOxgGnZlrtuJbkkqdjZVbepIDuhrpdzYTMmpdFrpzZjKYvYQyRaTi0YZNjRyLCrjSYTF2gd0G2Tj9PMXq14aMPOpkK3VUnOu0+jNzViK2+pKrhO5lCv3ZJ7Z+WfO5vttVyZCSlvzorJznnMYv4M5CuA+Yw4GtgTMt6Vuly9t1aydjnJ5hOqbp+dZUfm3Rkyy0Y+jSKvUNAEVWPbcS+B/30DyFb+0Hkjl95rxSpL/ORNa9i0bCpcSQRHEoosce1rVvNo53V8KnEFyu6HCd50NpYDT8x21wRzAFVV7aqq/lBV1UOqqh5QVfUjZdo9oKqqbvLvvsx+WVXViMn+QLlrp2VrLusagK7YCTefyt7QupIYmxFHI7IssbjOhcuusKLIUgWQDI0qfEPOBeiuEJ5GY/U/6z4ZdjQie8zfo3pWOM2TxrLxXBZFMo37CRZY1cYhRVRjydZ11i2uMU0IYpEl2mqcdNS6CvqrA7rVTdRWW+IiBmC3KHQWCeuxZRfhtlmQZajPEw6L6x4NuBfTW2sofgtrXQX9ylo8y63wL6x15QRFvUgkOuRZRkMZlyjd5kW3Gm6mKX8HYCjbZivrG5eGsEyi/pJFlkFWiAVVdEk2vd963n3L3xu211HvsVOTeZ7zBdpy9cOGnc2klUKFTZEl6gJ+JMWK7giC3V9yXHuNE58vWBLXJgFLysQ+jRWX2OetXLPsuLYAVkVm2LOQXt9KDgSPp9e3kj5PaSr1vaF1OcG+EjqGUjcYNL+2x25hOGhYyHz+AOTNaTXftLTbyCyYkoxny24xju/1l4+Tiy0+1/xc9sLvzKBrIVF74WJAfUY5Mn8G9YLFG7XBzcK8OK/izK4SErvrNrC/xlB0Fmc8BMKOJvaGTi3b/0ou2OkymT8HTWJ5dbuPg4FjSFpc7K7byFDrZnS7r+IKTjozz5jUZcsurpkpXymbMbeKLLG6xUeNy3D5XRhykfYbHgg101hCSihogrHRdZxP/wjfn95G3L+Ia31f4SN/1zl2QYCfX34sS+tmPmuX4PBAliQ+tGkJ1rVv53XRz3AomibwxzfgfObHlfM8C44EbgBOATYD7wI+qarqZSbtXg805f3bDCSBr2f2LwLsQEdRu4FKF18QcHBUs5dEwzEkmtaSkiwkLKMuPonGY7H6GhlwLyTotOK2WVgScuO2W1gUGhVGdauLVM2oS1RatpFYsI6GmgBnqCEcllHhLivEFAfy97uXELUF0TMCg9uu5Fbnh0LHFyiAYAiX5eoVZdOcAyytd7Mg6MgpFZIEydBRpJ2V01CnAotw2yxlXQMDTmtFt8ERZ7PpdqsilRh4LLLEUU0+rEs357YllNH5UWTJKOfhMJRbu0U2lMMM7TUuVrf46PcsZXfdhtz2ZfUe1raN6ujLGtwsa/DSHTgGMCwjumw+xsEFm4i3rkPPKDEpv5HsxWlVTJNYFJ9CLtW3q0PXCxLDJBUXWZVA13VALxGEu4PHIUujVjY5037I2VIgGGcF8X7PYvoDRxHMc+tK1q3CjCFnYfKKdCYuslhBACaeNKzcJGWNynnthlxtIMkMudrQZcuolSnz/4TFQ9JS6EZsfmrj5PnPmXEiGWdGocz+POWnx48tuwjvMZdULnyuGws2Q61n5ub/qIyrX3PHSkYcDbgdo/e4bVHGiqSYz1/Cb8SQVVP4ujUwer+XN3hZ1mCMb3mjN5ch1m5RkCuk6pQkwzIcs9Vk2ss55SbfhTJLNZa6Aut+5gWQzQIZz8u4GG/bQLxtY+5ze0MNDQEvybqjcm6cRj/cBe6Qg652hmuPJuVfmNfGSdQWZEmdm9UtPty2Uit0zFmfGfOoLFLrtuG1W0iGOlnV4i1IIDXVzERhDsF8Jj6M9+6rcLxyOwebzuDSnrexcwT+87SFXL52QcVVEYGgGiRJ4j3rFuK0Kmx6pJ6f+H/K8Y98Fuu+xxja9FV0k5VaweGNqqpu4ErgfE3TngCeUFX1euAq4Hf5bTVN68s7TgJuB36uadodmc0rgV2apu0c67rD7tGsitli1ImM8G23JIHRFdO0rw3J18amaD+2XXmrqEULC9nYEpeJAJC1hKSK3MuKA/ljtiAHak7kaMXKpmUhZEniwFAUACXQTNoWKTyvBLq/DfZtKdiuKzbi1kCujdOq4LQqjMRTvGA/lmCNjtvuI9G6DvtLt5rOkW7zGivW+WPMjLnYjTLtqMGiDJNM6SWWhcLF/Iz1R5JZ2eBFH90yet0y74Ggy0rPcNx0XzEpxUmy7ijSdj8LXYWCusOi4LDKo9bKDLKJTSRlcUOFel/F5H4n08Yz1B50sr03UuJ6Zwj2JlkQM5scVgUkmcjSC+nevY2orQY9c/66xjZGDg7gsipEk6mC4y2eWoiEc+da3uih3+7h73mVTjpbgkT6wjiWrkB31iC9Mvocp90NcPC5goUGMNxEk4qLmqEXjZi+9nV4MNxuh51NeCL7cwMofQLMSVicWJMR03358kaDz86B/tLvihkhjw23XWEklsKqSCRSOv2exQSGX820KOxb9iscctvQeyBir+Wgfw0nLgzlYvGys1M8LkWW6Gz08tSegfLWQUkCqxOIE/LYcsqELEn0+FdTp78M0R2kZQVn+6hLXnzBqSQPPMBB/2pa+h+vWFsvagviiJcPGfA7rSgJhQQ6iqcOa2QfyfrVWLqfBSBmDTDsbCLZcExBtmWnVWFxyM2rPSMApHytSOlk2eWuiL2eXXWns5pRzxhZhrg06v55UkeQ4XjmmZUVekIn0ZM0rHj7a06iveuvgJEsJZ9R44BEY2MzuyybAIm0bOUcHhltKEnEva0Fyv7e0AZjQYi/oVscSMkoyaLES3rxIkA+smJqyZ5KhIImKIvSvw3Lze/E0vMSdze8k3fv2ECj18GPL1sxpt+4QDBerjixjRq3jcv+6uFaz0revf0XBG8+h8Gzf0CyvtQlSnBYczSG1SvvV5ZHgE+pqmrRNC1Z5rjLgOVAfo7slYBWzUX7A50wYi7se+wWTl1UUyIQZlPu6xY7UnJU4u31rcSfeCn3Ob8IbLFgXqwUpO1+YNC0H1lhrsXvxGFRjCQOEUOgtVpkEsk0siSh20etQz6HFaKmp8sRswWJe8unis9ai9KOIs9QSTaC6C0K9jo3cf9pSPEh0hnFti2YYltP2Eg6kh4V+jubfAxGEwAkbAFIQcrdhK1/OwDxCvW0DnlVWuNP0DOUd6+qNLingqUZ2WKLzkZKJWiw2Rn2O0jnybV1XhsjA0rB3dCrvViG7K2XdEMItWWspoF8N1TZglWWWLtqFY9tcZhmv8t/Bhs8K3h85yHWtvqJWc/CaXGw2vpP5OGRkoXTRONaPIP7URSJ5oATa79sZKHLU9DSTcfhcteTcpYmjdAVG7FlFxVsyz7DxXFo2X6G89uWnZlCDgTXErd4aTt4f8m+ziYvdckA9BnrLM0+O41N9SXtikl5mpEliXWLahmIJOgejrGtJ8yQc0Gegmb0saPWyY68Y5fVuxkacPBqxBD6862AdW4b+wC/o1SMrvfaWbPAT73HBpnlo+FgJww/NWZ/dUlh2N+J3L2jxOVWd9Ux2HYGsUiS3Y1nkUzp5JaUMtpEtgTGgeDxuefN77QxAtgV8++U3r6OuLsPEnl3TZLo8R+dceU1FjP0jPVxSd2ogpZszGThHOguP6YiRXJxSyN7g6dA7z2AsfCQn/nbFWhA74vk5qPPq5KweCh1Wh1leYMXtd7D3S8eBEoTipi6BQOxhWcbLqqyhe4dhyCaKmmXT/H3YDoRCpqgFF3H/uLNeB/6FEnFzkcsn+SPO5dz0apGPrBh0ZgByQLBRLngqEYavHb+63aFf1oX8/3EtwncciEjJ14LGz84290TzBxNQJ+maflqRRdgA+qA/WWO+zjwfU3TuvK2rQS8qqo+BCwFngKu0TRtTKVNtxQm3zB991kcxBafixQfwjZFJSMSrevZXUWNwNpc/IMhnDX57OzqixBzNZPydxDy/BNJgmDQAf0m12k5Cevex6rrlMVJvG1jLt2+cfzJpG1e7NvvZkHQxSFFRnfWoOcJ+UvrPLmV7mwMzMJaF/SD22ZkamxvbiHmMNzT4h2bkUcOFJyjmLjVz4hrBQcsVkIZq11tnjAMsLYtwEg8BQerG5tucSIDar2brTuMzactqUXen7HWRMa3Wt7rW0nIY6NnOD6qkGfixOwWmVUtXiQkciqmw0+syajD1u8tf63sM+h1WNisZmMWC+NgnFaF1qCT3YcylijZAnXL6ZQ1EpnjrYrE2SvqeXR7H0PRJCh2U+UVMI3dMSOrGDqtSkUF7ZSFNTy6vY9iovYQ9V47S6XSeLUFASfyYGVrmSQVGrDjSy/AkqeU+J1W0rrOtp5wXnZQKWdZyaXS0EfHE3LbeNXEoBdwWgm1+EjZFMxWi4oLOrsalhHf/VRBQqGJcHxrgGgyzeM7D6FLei5JR9Blw++04mkN8fy+oUxyEONarQEn6QY3drtcoJTnBitbjMQlGUUq6VkAeWNO+9qIW5wF2VzL0RU8tiBpD5RaGe1WK621Hug1P4da72Fn32gHBt3mGWeLyV/4ysXsvWSstph5ba5q8oE17z5JMjCqoNX7vcT2yzQEvDAL+cuEpC0oQIoewvPAx3C8eicvO9fwlkNvR/a38L2Ll3J8m3m9GoFgKjmxPciPL1vD1X+0sGHov7mp+Sba//4l0nvuR974NdK+sWuvCOY9LkpFiexnU98yVVXXYVjPzinatQLwAO8DhoGPAverqrpC0zRTx5xs/EMyVGVxW8WaU1xSNUtL/fMyFCcPweIg7WnkoL/o3SpbWNFabwha1VCU2GMw0AmyQnMmTi1WJqYkm6wgG682Vrp5vch6NlZq8GIsssRmtc6wKPUb1z1tcWGMim7zkLKZKwrxtg3siRm3LOJbTDQRxmFV2LCk1nCvzJP5a902at2gD3hIu+rKCoPFpDOulEPO1pw7WzFjhcfuqj+DtGzlxBY/iVTpw5B9VmIdZyBFxu7YRBypgi7rqIIGpGpXkKpdgTywo6DdSR1B0unSAUnjjAFeUufJufS1B52E6t3s3YWpZbPke5BHnceGM2ycJ1m/umgpJj/pTOGsbFxWT3g4ygMv9wBwhlpn+jwHXTbOXlFPOh6GiJuXDuVZTLJWwUyn05mYzxFHY9n+VkuD105zsw+LZLzI/A4rkpRZrMhrs61nhLaAnT15/cnHosh48qxDiiyxosmLRTasRC1+Z+69EfLYjCyxUhyHRSm4FanAYpShfaTdeVZIi4PYsouIhhOws1AjqUY5A8OlcUHQyXA0SX8kUbCv3mc3LLdZRbBmmelCzJRkA89Y/LNKe4u/tE5h8ffbZVMK+mwJLWHxKgtpfwccemHyfRonQkET5LDtuAfPAx+F8EG+qr+ZHwycy1tOXchbj2sp+0MlEEwHS+rc/OzNa/joHVvZsOtt3LD4RN7Y/S2CvzuT4fWfJ7b8kglE2AvmEVFKFbHs5zDmXArcp2nanqLt6wFF07QRAFVV3wzsBi4EfpHf8KJjWhiOJBmIJnB6V+AsVw+pHLVvxgmMDERwOK3YFStejwM94ILlp6FafXQorsJ4tOAmlGf34QICgVFhLRBwsW2gUEfN31+AYkdy24lLErZwAovHYbRdfS5EB3B6XEhuOy4pjctuWEMkJAIBY/uiQC2W2jraa0ctF5Lbjs1hweW2lb9uXltZlsZsV3wMgLOKc4+2c2HrlrABXq8DVzSJz+ugsc5b/pxrLgDA9ew+oMIc5nDRvfiCXFup146esGGVPdhSFiweHz6fM7cPxY7T7wKnK3f9c5caK/7FcYfSkBekQfSFp4CrFheAdSQzf3Kub668zHC1bntufGNiW420czAX62Z6/3Qn0rAdvJnnsgyS2wq60f9y98jltpFyLSJujbHw6JMKLG01I16G++xEEim8Hjs47bgy2fQCAVdujNm++YcT9CfTBPxOXCPGPOodR9EYP8hgNJFp50QaytxjnwO8o/1SFBmvw4Jrn+GMGqoxzxqZIwFSwIU9EsXptuOUbJBI4bLacLvtmeu5IHQZ6ecOlHw/STmQwnZ0jwMqPVOdZ0IyZjwjx70B0gmcNqP9JaHSBGsXhTyQiDDiczCUlMs+ry6XnWQ6jc/nwu4djedy5s3tGasyyXiiA8azabeDJTN/jc3QeCnOzNwVvnvguYwLY9nrF92//Gf21OUN9I7EefRVQ1lG13FZ7CzKfD/01lXgc0GgMNun2fkB1AYvNkvp98Osb8X7fF4HiVSaQMCVi5HNtvH7nfjy3FZP8Tn50/PGioDH4yAQdEPQSMBk9m7JbpsuhIImQIr04nn40zhevo3tUhvvj34OT/tx/GbjYtYsDtHfX04eEgimj5DHzvcvWc03HtjGh5+WeLD1O1yvfA/ffR8itv1uhk/7AmlP02x3UzA97AWCqqraNE3LeoE1Yiw+l/pGGZyDkfmxgCI3STRNi6qquh1oKW6bSunEwjEcQP/AGEFbFRgYjBKNJIglEgwNR0n2h4GajA0wTjxcGOcWzsS9Fb9rw0XxcOXexVJcwjYSIxxJEI8miQ7HMm2tQAgGwthHYoQjOuGkcc4T24P094eRak5At3nwK1LB+e0jMeLRJOGR+Ji/AYpzCT5rZFy/FYq9HXSd1BjH2EcMJTWWaZedk5omyYgByut3cdt8ys2xGfltrSMxopEEh9I+BpxriMq11A5G6JdBci7FOvgk8YgCsTCKaylSbJBk5v4W32dcy1FSHlJxJ8QzSTvCEWwjMVyudK5vfovM/szzt9jvGMe8OqHpzFyCl3qnBW+tq+B4Ke7ENhIj4Q+SrnBeW8KGFDOUHbP5zJ+nsHUZ/UNJyHP2U2I2mj02BvwrCA+9SDoVIxwendd2r50dfeFc35qdClGHBRc64bz72BlykUwZcyMPRrFm9iUGI6RTz4licAAAIABJREFUo/0KBIxxVn2f0ynsIzESfpXwSJyoEietQzgeZ0mgcM7NzqkMRrGMxEjZst/vcriNf7k2MoTH6FsiQqvXhm4pf+/D4RjJlM7goIR7ZHQhJ2YyB1LMeMb0RJS0qwWlf1vBPc3OXcH5x5jH4v3576r+/jCJRKpg20DzCtLOOrC5DQ+DMe5P/rH1dsX0WmZ9K+lXOE4imWagP5zLrplrMxAmHbOaHu8MOkveh1D4Xchum66MDEJBO5LR09hf/AOOhz+PlBjma4k3cqf3Et53tsr6RTWi6LRg1rEqMh8+YwkrG7186Z6XWW/9ID9f/g86X/4Owd+czsjJHyXa+e8FdWgEhwVPA3GMNPsPZLatA54wSxCiqmoIWAw8WLTdAuwEPqRp2u8y2zwYsWgvTlfnwXCLSnCAZKBMXM84cdvLP+O6zWsEu2/9k/G5xCmuMMZmQdBJIFtc2lkmDfY4rIepmqWGNWYcClqqWvfRMtgtckGa/Cx6lfFS48FtUyA6mj4+6/2nu0LEF56Va1c2hiuLYiMVWDTm9Vr8jpyC5p1AzHcy1ImlZws1LhuxojpNut1HbOmFY3ogJBqOwbbrwYptKpGqWYbkqsNrccDQi0YGvrzHoy3opC046nZmUWSW5dWvS2dc6iyyhGU63u+yQmzZRSzRddoSKeQdErIEZy8bO/HItJNxAUx7F0zJ6XSbh7SnkWSNiu4ITknSreJ4P4dVJppI59ylHVaFjUtD7OgN0zUcM9wEJ0CNe4LlGSZJcQyhGfH205ESI9PWB6GgHaFYup9Fufdj+Pqe4Yn0Um6wfoZN69fx686GSRXUFAimg3M7Gzh+SYirb3qa854+jnet+AnXxG/E+9AncWi3MLTxfyYt8AnmDpqmhVVV/TnwPVVVr8Cwnl0LvBNAVdVGYEDTtGyQzVFAAnip6DxJVVX/D7hOVdV9GNa36zAiW+5gGknLNnob1tNqG8PVCjihPVCSdQyMzHUBpxWnVRnbo9fqJFx3DImBJwtqZU2UePsmDg69MunzzCSxjjPL1os6rtVP70jCdF8l0p5m5Gg/yxfU82xXcVjkVDJ6g2vdNs5aXkdKHzsu0IxUzVIsPVvKN6hi8TVbc2/CSFIuvijettEozdDXU9WhscXnmi+6TcOisSxJOExKYOSzcWkoU2duhlCsxBa/FuSxlRO9ijZIMonmk8ZuNw42LAkVxFduWBIiVRTLaLfIqA0e1Ibx18o9Y1kIWZbGXcrJY7cwHCtN2zIdd0+3+6e1DJBQ0I4wpOgh4vd9gdrtN9Ore/mk9F5CJ/0bXzlmQUkNG4FgLrGswcv/e/MxfO+R7dz4xF7+L3gN3znuPFa+cD3Bm88hsuZKwms/UJBlTjCv+SDwfeA+jJzzn9c07abMvv3A24CfZT43YChsZuk53g98GbgJwxvlHuA1FVL1zzhBl7nVZ0GgNLC9EklPC3tDHurLCjXViym6zcPaVeYFiucsFZThkMdOyDP+mJFUzTJS/oU0KVYSUoStB4YqWjOnCkmSsMymE8s4rFZjydDZ5DKdTV6GTITnEsoo2RNLlzJ5ZkU2qtYSbHEQ7zgD2457c5vUBg+uac4bYLfIJfOiTGAxoRyVDAXZ7KhmHN8WYCQ+8Ve7tYqC3zOFUNCOFFIJeh/9MS3PfZtAephfcw7dR3+At5+girT5gnmD3SJzzcbFnLKwhi/830uc97dW/q3zp3xE+Q2+p36A48VbGDnpw0SXXyrcHuc5mqaFgbdm/hXvk4o+34ShgJmdZwRDSXv/NHTTFHdmRX40lffMEHQameE6asan2JXDMQeSQyVrVZBHhdWA01qSHW7aySgMbUEnIbetfAFiQUXGu+Awl9Ezvy/FNb5mivwXYPGiZEdN9cl65iNrymRHBaPYu80y+r6opG6ZGUVPXxqaZO+mDiGZH+bo6TS7/nELzU/fwPLUPv5JJ893fpSNp6wXiplg3nJie5CbrljLjY/u4DdP7uXProu5bu25bNrzLbz3fwTnsz9jeN1nSCw4dba7KjgC8TosbFhSO+MKjs0ic9Zykxga2Ura3YAUaocuqJ1hxXEypGpXFHw+ttXPSDw1azHSU6+czaDr3DiJt66vaMk5qSPIYztmrkBU2jF3Sv2kfe0k0ylSgepqdE01rUEn23rCU2q1mi8osoQyTQuwcyn3gvBpO0zRdZ0t/7qXnh+ezdonriGakrld/QoNV97FeaefLpQzwbzHZVO4ZuNifnb5MdS5bbz7EQuvj36aZ4//KlJ8kMBtl+K76z9Qeqc1F4RAYMpcsD7lkCQSLSfjqWnmjGUhGn2OsY+Zo1gVmYBz/iiY8xndWVvRZbxSPbNpweok0XIyMFqvbtaQJFLBxeNKpjOVLK3zcNbyulyMVrJm2axZ8+YDZjGEc0gXM0UoaIcZybTOY0/9C+2Hl7Dx8bdSn9rPg0s+jv3Khzh582U4xwiGFQjmGysavPzs8mP49NnL6BqOc8HDTbzH/wN2r/4g1n1/J/i7M/HefRVK/7bZ7qpAMOuIJFBzC91RQ9oZQm9aM9tdmRek3Q3Ell0ElqlfZNCt88s1MN/akwqtJL7kvFnszdxkRYMHqyLlUuwDuQUeizy334VCWj9MCMdT3P+vJ6l55ruck7qfhGTlyY530XzGNax0jD+DjkAwn1BkifOPamSzWsev/7WHX/xzN6e9spYLFp/KR7x/ofmVX2J/5Q6iyy8mfPw1pL0lJbAEAoFg5pEVEq3rwOGCqKg5OlvE2zbMOwVNMDaNPkeJx8DRLT66h+NzPpZUKGjznJ7hGH95/CkWbP0Bl3M/SBI72i7Gv+kjtHrmQD0PgWAGcVoV3nFyO288upnfPLmHm5/ax23x07mgYxMfdN5Ju/Z7HNotxJa9jvAx7yZVs2y2uywQCASCWUafQ/FtgunFYVUKavDNVYSCNk955eAIf3nsnyzb/jPeJ9+PJMl0LbwE52nXEPA0z3b3BIJZJeCy8t51C7n8uAX87sm9/P7pfdwePZdTak/nE4G/suKV23C8eDOxjs1EjnkPiaYT5r5DukAgEAgEgiMCoaDNI2LJNPe+dJDnnnyQjX0382n5cXRF4dDSS5BPvga7txnzxKMCwZGJ32nlXad28NYTWvnz1m5uemov5756Ia32M/lU3SOcvu9WAjveQKLhWCKr3mIUSLXM/ZU1gUAgqETaEUSOzlyGRYHgcCbecQb6DKftEAraPGBHX5hbn9nD0At/4fL07bxVfpGozcPAynehH/t28DQJxUwgqIDDqvC61U1ctKqRJ3YPcOtz+7nqZTdyagPv8/+dy/v/TM09V5N++DNEl19MdOXlpGqWzna3BQKBYEIkFqyDtHkx36lmVgo5l2FxyE0qPXdLF8wXltS58YtsqTkqZTOdLoSCNkfpC8e5R+vhiS3Ps6rnLt5reZAWqYeIq5Gh4z5NbOWb0W0i+YdAMB4kSWJtW4C1bQEGownufvEgd70Q4qv7N3Cy/ALvsjzA+md/huuZHxNvOpHYsouILX4turN2trsuEAgE1SMrIE+vN4AsSXQ2eal1l6+VNtMsqXPPdhcOCxaHxDzONkJBm0McGIzyyLY+Hn1lP74993Gx/ADvUZ5FssJI8zoGVl1OfOHZoIhVDYFgsvgcVt64ppk3rmnmwGCU+19ZwjdeOplr9+7mDcpDvPnAQ7Tv/xjuhz5JtOlkUuoFxBadI4LJBQKBIMOCgHAJFwimA6GgzSKptM7WriEefrWXf7y6n5a+RzlXeZwbladwWaPEnI1EOj9AdMWlpH2ts91dgeCwpdHn4E3HtvCmY1voGV7B/a8cxye2vYXhPc+xWf8b5+19jI59H8F9/8foC66GjtNRlpxBsm7VrBUqFQgEAoFAcHgiFLQZJJlK82L3ME/tGeDJPQN07d3GmuTTnC4/zUeUZ3DYYiTsQZKLX0//4nMNH3J5btdpEAgON0IeOxevaebiNc0kU508v/9cfr2zj4PbnmBp3wOs732G1Ye+Bk99jSHZz77giaRbTsC98GSczZ0gi9eqQCAQCASCiSMkiWkildbZfSiC1j2M1j3Mi93DdO3fwcrUS5wqP8+XrFtolfaDFZLOOhKLLzWUsuYThYAnEMwRLIrMmgV+1izww6kLiSRex4tdw/xt107knQ/ReuhRjuv5Bw29d8OzEMbBq1aVg/7VJEOdWBs68TUtpdHvwjaHAukFAoFAIBDMXYQmMEmSqTTbe0Z4bmcfO/vC7DwUYefBQcI9O2hN72aVvJ1N8nauUXZQoxwCBVIWN8kFJzO84F3EF6w3iuWKGkwCwZzHaVU4ZoGfYxashlNWA1fRPRjlmd0vk9j9D9wHn6J55HlOO/hrLD1peBEiug1NX8BOpYNDjjZinlb04ELk4EJ8vgDtDT7s6TS1bhsum7CYCwSzzYkdQRxiQUUgEMwiQkGrQDyZpj+S4FAkQV84TtdgjO7BEYb6e0gMdcFwN1K4m1q9n0apj2OlA7xR6aKZg1gsKQB0SSYZWEyq/gyG61aRaFhDsm61SPQhEBwm1Psc1Heugs5VuW29iTCDe14guv955INb8Q9onB5+El/kPogAB412PbqPLj1Itx7gZT3IITnIiC1EzFGH7qpDdgawugJY3UE8bg9+hwW/w4rfacXnsOB3WLAoQpAUCKaSgEgvLhAIZpkZUdCiiRQpXUeJJhiKJknrOjqADml0dB10QNeNv9O6UcNCx/hb1ylpk92XSuukMv8nNoyv55+kUyl0PYWe9/+0niadTpFKJkmmUiSTSUhE0JNRSMYgFUNKRklnPsvJKC59BK8UoZEwS6UwXiK4pJjpDCYtblL+DvTg8cT8HYT9C0kFFpGsXQE2ka5UIDiSkK0uAgvXwsK1uW0xoCc+hDywC6l/O4nebaQPbachcpCGoQM4Ys/jSvQhJ9MwjPEvj5huZRAXg7qLIZwMYOOAbicu2UjKDpKynZTiIKU40BUHksWKolhQFAuSYgHZiiQrSLLxWZZlFFlGliR6vMtZsfJYmnyOGZ0ngUAgEAgEpUy7gva37X188I/PMxN1Az9s+R3vs9w+oWNjWElgIyHbSMl2Ug47KauHlDWEbveSsPsYdvtJuQPgDJB21Wf+1eFrbqc/LFaxBQJBZXSbl1RdJ9R1Ii0FBfAHXPT3h4kAkXQKKdKLEu5GivQgxwYh2k8y0k8y3E8q3I8z2o8jPoKeiCIlIyipQSzpGJZ0DFsyijURw0pyXP36R1rld9Efcs3GxdMyboFAIBAIBNUj6bqouC4QCAQCgUAgEAgEcwFh9hEIBAKBQCAQCASCOYJQ0AQCgUAgEAgEAoFgjiAUNIFAIBAIBAKBQCCYI0xpkhBVVe3At4GLMZKWfU3TtOvLtF0OfA84CdgDfEzTtFumsj9TRbXjUlX1AWCDySnu1zRt07R2coKM856tB74BLAdeBj6iadrdM9XX8TDOcW0CvgIsA54GrtE07Z8z1deJkBnfE8DVmqbdU6ZNO/Aj4FRgF/BBTdP+PHO9nBjVjC2v7RLgOcCradr4MmPMMFXeszOALwMrgL3A9Zqm/WTmenlkMp73xZGC2fOqqmoNcCNwNtAHfEbTtJ/nHXM08APgaGAr8O78d6mqqpcAXwSagb8CV2qa1j0zI5oZVFVdjPE7uQ4YAW4CPqFpWnSsd7KqqqcD3wSWAP8A3qFp2it5+98P/BfgB/4AXKVp2siMDGyGyMiG38GQDXuB72iadkNmn5i/KlFV9cfAEk3TNmY+T/i7qaqqBHwBuBKwAj8B/kvTtNSMDWgGUFX1zcCvizbfpmnaRbPx7E21Be0G4BRgM/Au4JOqql5W3EhVVQ9wD4ZidjTGl/G3qqqunOL+TBVVjQt4PdCU928zkAS+PkP9nAjV3rN64A7g98Bq4Gbg1sxDOxepdlwrgL9gvJCOA24H7lVVdcEM9nVcqKrqAH4LdFZoIwG3YfzAHQ/8HLhFVdWFM9LJCVLN2PLatgJ3AnM+N3yV92wpxnj+CKwBPg98V1XV82ekk0c21b7jjwgqPK8/A2oxhJTPAzeqqnpK5hg38GfgMYx36cPAXaqqejP7s++hL2AI3z7gF9M9lplEVVUbxu9kDON5uhy4CLhurHdy5n12O/ArYC1wALhNVVU5s//1GHP3XuD0zDm+OmODmwFUVbViPEO7MN6B7wM+parq5WL+qiez0Pf2vM+T/W5eA1yBsYD1OuBNwIenexyzwEqM3998Of6K2Xr2pkxByzwAV2Kstj2hadptwPXAVSbN3wIkgLdrmvaypmnfAu4GTp6q/kwV4xmXpml9mqYd0DTtANAFfAn4uaZpd8xop6tknPfsVABN076sadqrmqZ9EaPk7kkz1uEqGee43gs8oWnaf2kG1wOPYvwwzDkyixiPAWPlQz8dUIF3apr2gqZpX8YY19srHzZ7jGNsqKp6EcbqfmystrPNOMZ1KfC0pmlf1DTtFU3Tfo3xI3n5dPfxSGac74vDnnLPa8YydD7GO+U5TdN+iiGQvDfT5FKM3/UPaZq2FUOoG8hsB3g/cIumaT/TNO1ZDDng7IwV/HDhBIwV9Cs0TduqadqDwKcwvsNjvZOvBJ7RNO16TdNeAP4DaAWy3jdXA9/WNO12TdP+BbwbQ3j0zNTgZoAWDOvD+zLvwDsxFvM3IOavKjLvsx8Cf8vbPNnv5tUY1vIHNU17AMMSNCdlpEmyEng2K8dn/vUzS8/eVFrQjgbswCN52x4BjldVtdiVchNwu6ZpiewGTdPOm6OuPOMZVz6XYbgCfmIa+zZZxjO2XsCvqurFqqpKGQHZCzw7M10dF+MZ1yLg70XbnmEOLhZkWE91ixknAU9pmjaUt+2RKo6bTaodGxguVp8APjCtPZoaqh3XzZQqBTrzwEI4z5noO/5wpdzzeiKwP99th8J3yknA3zRNSwNomqZjCIn5+x/KHqhp2m5gp8l15jMa8FpN0/LLzOsYz9dY7+Ti+QkDTwInq6qqYKy6P5R37GMYYSrHTPUgZgtN03ZomnappmmRjJxxKnAacC9i/qrlOuCBzL8sE/5uqqrajKFs5M/dI8CCjOXocGIlxne4mFl59qbyx6cJ6NM0LZq3rQuwAXXA/rzti4GnVFX9Hoa5dD/w6cxqyVxjPOPK5+PA9zVN65rm/k2G8YztYQxX1JuANEaN3XdkVmPmGuMZVxdQ7M7YDoSmtYcTRNO0G7N/q6paqWkTsK9om9lY5wzjGBuapr0n027j9PZq8lQ7Lk3TXsr/rKpqA8ZCz+emrXMCmPg7/rCkwvM61juliVLhpgvDVa2a4+c9mqYdxLD4AJBxcboK4/ezmvkrtz+AsVCT269pWlJV1V4Oo/krYg9GPNSdGDE730DMX0VUVT0Zww3xKOBDebsm891synzeV7SPzP7dk+v13CDjnrwYOE9V1c9jGLB+D3yGWfruTqUFzUWpu1H2s71ouxfDf7UfeC2G0H+rqqrHTWF/porxjAsAVVXXYVjPvv3/2bvvODnO+vDjn9m+e/100qlZli3jccG2bIMxLonBGBswprtRfk4cAoQeTC8BAiGBhA6hmBZIMB3bFBdccJdl2bL6SDpJJ11v2/vOzO+P2d5ur2nvpO/79bJ1uzu788zszOzzned5vs8Clms+zGTbWoCTsPrRPh+riftrqqouui6OzGy7bgNeq6rq61RVdaiq+grgGqzK2VJWax9UPWbF4pLtpvJbrIv6d5pcnGPdjK/xx6la+8mVHaMx3TXneLwmfRnrLvlHmdv+8RU9rvX+Y82rsv+dhzWOX/ZfHdmkPj/A6qrtL3t5vvfdsXh9fA5Wo1UUeD1WjPJGrHO4KcfefLagJaqsLPc4VvZ8BtiuadrHso+fUa0Mgf+INUB7MZnJduVcB9yvadrAgpVqfsxk2z4IuDVN+1T28TOqqp4JfAK4euGKOCsNb5emafeoqvoxrLEULqxm6W9S6Du8VCWwsgUVc1P7mBWLhKqqHVh3jU8GLsl2lxALZzbX+ONRrf0U1zTNVFW11uu5fTjd68eMbMD6Vazxea/XNG1ndv/UuybX2j+T2deo8foxt/8AsmN1UFXVh5WU4YfI/qvnU8A+TdN+VeW1uZybxfsuXfQ3HDv7juw52qNp2mT2qWez5/HPsbI3HvVjbz5b0AaBrmwzYc5KrChxqmzZIWBP2XMasG4eyzNfZrJdOS/Duvu92M1k254P7Ch7bgtWJXKxmdF3lk0M0g6s0TTt+dmnDy10IRfYINY2F1vJcdZda6lRVbUHeADrvLpM07S+JhfpeDCba/zxaLprylxfPyZkuzX+EHgHcF026QzMbf/kKnr517PjI5dxDO0/VVXXqKp6TdnTu7Bung4j+6+eG7ESe0RUVY0AHwIuzf49l2NvsOgxZX8fK/sOgKLgLGc31rQCQzTh2JvPAG0rkMJKLZtzCVaGvPL5iR7HarYudgaLs1I8k+3KVbA2AH89OsWbk5ls2xBWev1ipwOLsQLZ8Hapqnq9qqrf1DQtrWnaSPaOydVYleSl7AlgY7arXM4l2efFIpQNEP6ANf7xbzRNqzZYWcy/GV3jj2NPAGtUVV1f9FzxNeUJ4KLsNTTXinRR2euX5N6YTTCwjmPvmvRfWJXl12qaVnyjdrprcvn+8WF1j3wim9xhc/HrWAkKMsAz874FzXM68FvVmtYn53xgHCspg+y/2i7DGnu2Mfvf94Gnsn/P+tzUNG0Ia9qD4n13CTCUTSZyTFBV9bWqqo6W3ag7F2soVlPO3Xnr4qhpWkxV1Z8A31ZV9SasaPEWrG6LqKq6EghqmhbHmujyPaqq/gdWOtBXYs0/c8F8lWe+zHC7wDpB0sDeKh+3qMxw274HPKaq6oewBuy+CPg7rDGEi8oMt2sP8BNVVR/BOok+ijXe7sdNKPqcqKq6HKu7UQTrBkE/8GNVVT+NFXReyCJOs19P2bYdM8q26/1YlZGrgGj2OAVIaZomLTkLZLrrhbBomnZAVdW7gf9RVfVdWMdqLn08WL8L/w58I5sA7K1YPRNuy77+38BfVVV9FKtC8zXgz5qm7TuKm7GgsmOy34f1O/JU0TkM01+Tfwh8UFXVj2PNxfRJrIrxfdnXvw18X1XVbdnnvw388Bi7Jv4Vq8Xsx6qqfgDrZve/Y2UmlP1Xh6Zp/cWPVVX1Y/227FdVdYy5nZv/DXxBVdXDgI41hdTXFnqbjrK/AgrwPVVV/w1rTNqXsv89SBOOvfmeqPqfsSq592MNbP+spmm/yL42THbOBU3TDgNXYF3Yd2L9EL5O07TFeiejoe3K6sWq/BtHt4iz1uh39iRW8ozrsFLrvw94o6Zp9x/1Ejem0e3ainWx+jes9PprgMu1aWZ4X6Q2Y1Us0TRNxxpgvQKrK+pbgNdomnaoaaWbm/y2HWOKt+sNWDfN/oJ1jOb+u6M5RTuu1LteiIK3YN1R3oQ15uUfNE17HEDTtBDwCqw7809jzZ358lxq6uxyb8Uat/w41jxM/+9ob8ACe3323y9Qeg4PY1X+al6Ts/++FngzVstHL/CqotTot2El6fpvrGvEU5Rm6lvyslMvXY3VurAJ62b+V4GvT/ebJvuvtnk4N78E/B/wm+x/Pwf+82iV/2jIdm+8EiuL99NYjRLfAb7QrGNPMU1z3jZQCCGEEEIIIcTszXcLmhBCCCGEEEKIWZIATQghhBBCCCEWCQnQhBBCCCGEEGKRkABNCCGEEEIIIRYJCdCEEEIIIYQQYpGQAE0IIYQQQgghFgkJ0IQQQgghhBBikZAATQghhBBCCCEWCQnQhBBCCCGEEGKRkABNCCGEEEIIIRYJCdCEmAeqqirNLoMQQghxNMhvnhALSwI0cdxSVdVUVfWWeficTwH/NA9FEkIIIRaE/OYJsXRIgCbE3H0G8Da7EEIIIcRRIL95QiwwCdCEEEIIIYQQYpFQTNNsdhmEaApVVU3gP4DTgCuBEeBrmqZ9tWgZB/Ap4CZgBbAD+LCmafcVfUZOv6Zp67N9898D/APwHCANPAH8s6Zp2xd6u4QQQohy8psnxNIhLWjieHcLEAJeDfwG+IqqqjcXvf594APA17LL7AH+rKrqRdnXX5j99xvAa7J/fwDrR/BWrB/BdwNnAD9esK0QQgghpie/eUIsAdKCJo5b2TuBD2ua9jdFz/0CeJ6maRtUVT0N2A28VdO0W4uWuQ9QNE17cdHnfFDTtP/MPv4aMKZp2ueL3vN+4MtAm6ZpkaOweUIIIUSe/OYJsXQ4ml0AIZrst2WP7wSuVVV1GXBZ9rk/Zbt95PwJ+IKqqi5N01LlH6hp2nsBVFVdjtWV5DTgldmX3YD8WAkhhGgG+c0TYgmQAE0c70bLHo9n/10NLMv+PVjjvT3AUPmT2buQ3wcuAWLAs1hdSgBk7hghhBDNIr95QiwBEqCJ411X2ePe7L+TQBAwgYuxBj2Xmyh/QlVVG9YdyUngLGCXpmmGqqr/hNU3XwghhGgW+c0TYgmQAE0c764Cvl30+HXAPk3ThlRVfQTr7l+bpmn35BZQVfUjWD9Eb84+ZRS9fzlwCvAFTdN2lK0H5G6iEEKI5pHfPCGWAAnQxPHuJaqq/ifwZ6yMVNcA1wNomrZVVdXfAD9TVfXTWIOnLwM+AXxR07Tcj1QAuERV1Yc1Tdukquph4H2qqo4COvD/gKuzy/qOzmYJIYQQFeQ3T4glQNLsi+Pdx7HuDP4RqzvGGzVN+0XR628EfgR8FLgLuAH4CPCxomU+DbwIKxWxA3gt1qDoX2bf6wNekl32hQghhBDNIb95QiwBkmZfCCGEEEIIIRYJaUETQgghhBBCiEVCAjQhhBBCCCGEWCQkQBNCCCGEEEKIRUICNCGEEEIIIYRYJCRAE2IWVFW9RlXVcLPLIYQQQiw0+c0T4uiSAE2IGVJV9SLgZ8gEnEIIIY5x8psnxNEnE1UL0SBVVd3Ae4G8V0eTAAAgAElEQVR/BaKAq7klEkIIIRaG/OYJ0TzSgiZE416GNXnnB4FvNLksQgghxEKS3zwhmkQCNCEatxk4SdO0rwMyw7sQQohjmfzmCdEk0sVRiAZpmjbY7DIIIYQQR4P85gnRPNKCJoQQQgghhBCLhARoQgghhBBCCLFISIAmhBBCCCGEEIuEBGhCCCGEEEIIsUhIgCaEEEIIIYQQi4QEaEIIIYQQQgixSCimKVNbCCGEEEIIIcRiIC1oQgghhBBCCLFISIAmhBBCCCGEEIuEBGhCCCGEEEIIsUhIgCaEEEIIIYQQi4QEaEIIIYQQQgixSDjqvTg+Hj7uUzy2trqJRJLNLsYxSfbtwpF9u3Bk39a3fHmb0uwyzFY8njLlu50dOS/mRvbf3Mj+mz3Zd3OzUL950oI2DYfD3uwiHLNk3y4c2bcLR/btsUu+29mTfTc3sv/mRvbf7Mm+W5wkQBNCCCGEEEKIRaJuF0chRIMyCRwTO3GMbcMe6sceGUZJR8DQMe1uTE8neusa9K5TyCx/LnrXKaDI/REhhBBCNFFkDPe++0ie/DKwu5pdGpElAZoQs6Skwrj334nrwF24Bh5F0a0+3KbDh962GtPZCnYnSjKEbXIP7ugIiqkDYHh7SK29hORzriG17jK5KAohhBDiqFMmNDBNlEQAs2VFs4sjsiRAE2KG7FP78G79Dp59t6NkEujtJxI/842k11xEpncjhq8XlCpjRvU09kAfjrFtuAYewXX4ATz7fo/h6Sb+3DcTf+7/k4ujEEIIIY6i4z4f4KIkAZoQDbIHDtDyxH/g7vsjpsND4tTXkTjjejIrNlYPyCo+wIm+7DT0ZaeRPP1a0NO4jjyEZ+fP8D31dXxbv0v8rJuInfdOTE/Xwm+QEEIIcRyxRYYwPF3g8Da7KELUJQGaENPJxGl58st4t34P7G6iz3sv8bP/HtO7DIBIMsOukTD7J6KMhJKEkxnSuoHXaafb52RNhxe1t5UNPS04bEWBnN1Jav3lpNZfjj1wAN/mr+J95rt4dv+C6As/SuL062WcmhBiQSmpMEoqgtG6qtlFEWLBOYeexHS2kDrpimYXRYi6JEATog7nwKO0PfAh7KF+4qdfT/TCD2P6ljMRTXH3UwPcv2+CHcMhjGwPAa/TRofHicOuEEvpBONp9OxrLS47F67v4gp1OZeevAyXoxB86Z0nE77i68TOfTutD32Stgc+hHvv7wlf/hWMtjVN2HIhxEz8df8EK9s9qCtam12UGXEdug+A5KmvbnJJFoaSClvjgRvp5SCOC0o62uwiCDEtCdCEqCYdp/XRz+Dd+TP09hMJvOoXpNdezP7xKD95cA/3auPohslpK1q56QXrOG9NB+qKVjq8DpSiikBGNxgKJdk1EuapIwEeOTDFfXsn6PA4uOH8NVy7cQ1tnsJpqPecQfA1v8az++e0PPIZum57CeEX/yepDa9oxl4QQjQokTY4NBlbcgHaopWKYo+OoHdtmNNnuA7dR6b7VPSeM+avbEIci+QmxqIiAZoQZeyBA7Tf9Tbsk3uIbXwb0QtuYSAK3/3THu7ePYbPZefajat57dmrWL/MV/ezHHYb67q8rOvyctXpK9ANk82H/fzimSG+82g/P908wBs2ruamF5xAiyt7OioKiTNuJLX2EtrveScdd72N2DlvJXrRJ8AmE0oKIY59rsFHUdIx9PYTZp3lVsnEALDFp9Dns3BiaTIlGYZYOiRAE6KIq+9PtN3/AVDsBK/+HwKrLuW7j/Tzy61DOGwKb7ngBN7y/LW0e5yz+ny7TeHC9d1cuL4bbSzCjzcd4SdPHuGPu0b558s2cPmpPfkWOKN9HYHX/IbWRz+L79nvYw/2E3rpt8Apg5uFmC1VVW8FTtE07bKFXM8Th6YAuHB990Ku5thlpLN/zL5SrZiG9YdNxvIuVvftHWeZz8XGtR3NLspxZSAQp9PrpNUtYcBiJVctIQBMg5bHv0DHXf+I3rkB/3V3c1/mbK790VPc9vQgrzyzl9/d/HzedelJsw7OyqkrWvnCK0/nBzdspNvn4qN/2M17fruDkVCisJDdReRvPkf40n/FdeheOm+/DiU+NS/rF+J4o6rq5cDNR2NdwXiGYDxT8fxENMVhf/xoFOEYMZduV3NoMTF0XAf+jC0yPIf1i+lkdJPRcLLZxZiVQ5Mx7t491uxizMrO4TCPHmisLuEY346SDC5wiUQ5CdCEyCRou+ed+J7+FvEz3sjhl9/Ghx8M8YHf76TN4+DWGzby8ZeeyvJW94Ks/qzV7fz4jedyy4s2sG0wxBt/+jT37R0vWSZx9t8Ruuq7OCZ20vmbV2EL9i9IWYQ4Vqmq2gJ8D3i0meXYcjjA7pFwM4vQHHoKci1aM2CaJgOBOOasuqeVBndKMtT4OzNxlEwSx8TOWax3bmzREWzBQw0vH0/r7B+XxBdHmzYWAZjlsWl56nCAwWD1GzZj4SR37x4jrVc/b3TDJJZa4M67ehK7vw/nwCMLuhrH2LM4Rp9Z0HUsNRKgieOaEp+i8/br8ey/k8hFn+DhUz7Kjf+7kwf2T/D2i0/kp286j7NXty94ORw2hevOW8PP3nweazu9fOTO3Xzunr0k0oWLb2rDywm86jZsCT+dv3udBGlCzMzngQez/03LMfwUttDhhSwP6Emchx+E1OKvXI+EEuyfmH053X1/wjn0RGMLGzqKbnVxvG/vBDuHw/RPza3V0RYewtV/P7bw4Jw+J8c0Tf6ijTMQmP/WUOfgEzhHtza8/NbBIH0TUSLJyhbbY9VEpH7wcjTkcmoYc2ionYym2DFU/YbNwUlrDGU0WT0Ie2YgyMN9k7NfeUOOTuIQe+Ag9uI6jZ6CTKL2G44DEqCJ45Yt2E/nb67BMb4d/0u/w9fiV/H2X23DaVf4wQ0bufnCE3Haj+4pckKXlx9cfw43XXACd2wf4eafb2UoWLhIZVY9n8Crf4mSSdB5+3XzVtkQ4limquoLgTcAtzT6Hnt4AOfI0wtXKMAeHsSWCOAI7F/Q9cyHZwdD9M2xlcYWbaw7mHOw0MiZa5xIlVXEn+z3c2CyrDyZOBhVKrOmiZKyWs9y/1ajpCKFv2PjNZcDyBgmumGydyxSd7mjwZinGGUpBXgHssFLKNG8Mtty48WrtKAV31xdKJPR1IzfkzFM+oputOwZDbN/rF6LfjZAm2kroWk2Vj8xDVz991c87T5wF+4Dd81snccYGR0ojkv2qX103HE9SibJgSt+ygc2+9g6eJiXn7GCD11+SiGjYhM47DbeeelJbFzbwSf+uJu3/OxpvvDK03n+ui4gm4r/mv+j4/br6Pz9tQRe+xuMlpVNK68Qi5mqqm7gB8D7NE3zq6pac1m7XaGz08rMqrRYXZq9nfUzteb4WqxMg7n3lz+uupzZghJ1Q4sLs8H1kEmCo3Z363hKx+WwYbc1due70e2stT0A4USavWMRTq0zxcBM9qcyGIXc8mkXps1JW5unZN3JwwEGo2nO21B4Ttn5Z2hZgbn+UusJuxcl4IYWD6bPg5JwY7Z5oVoZAodRxjeDtwuz6ySU6B6rDC43virLpzIGvhYXDput6j6ZKbvdNodjL4LhsNHR4aVtlmOk+yejbBuNcNGGHpa1zC5rJsBwMEG3z4nbWZRxeHI/uFqhrfJ3qt5xVU/6SBBfi4uODi+dre6S/VeTac5439bT2uomrRu0t3tLtjeSzLD58BinrWznOdNMu1Fv+1taY6RsCm0dXjrLvpNwIlP63vE90LICfPWTEu0cCjEUS+ffO57UUaIpTlnhxtvuhdaycuhpa5/Z7FXPg5om9qKEtmO2uaFzXe3lEkEUR+GalvteFJ8z/3jPSJg1nd6SKYmmc2gySjylc/qqWfaA0lMQm8ofs+PhJD2trpJplDB08B8ATpvdOqYhAZo47jjGt9NxxxsxbQ4evfBHvPvuDKlMlM+8TOXlZ/Q2u3h5F5/UzU/eeB633L6Td/96Ox948Sm8YeNqADIrzib4yp/RcceNdNx+PYFX/xrT19PkEguxKH0K2Kdp2q+mW1DXTQIB6868O2olLkhmH08nlr2bHQjEsIUHMacCxN3L859XdblQAmc0iW6PkQnE6JuI0u1z0uWrXkFWEn5ch//KA7GTcHWfyPPWdVYsc/fuMZa3uThvbeVr1TS6ncXlLnf/3nGcHifddusGU7lUxsDmj9Hisje0P3NlAoinUxg2k7AnQcDrwBY6AqZBLGplsw33bcExqZE89dXW+6JHOKhM4bIrLFcSuKJJDDOBoSdwRJNkPHF0V2UZHOPD2KNJiI7AxEj+eTPlIFWlzGndIBZN4bArVfcJWGOE/qKNs2F5C6f0tNTd5s5OX81jTzdMTKyu8NVEo0liSZ1gMI6eSDOWTbqxos1NRjfYMxbhtBWtVb+bnEMjIWLRFKMTEexpT92y1pLRDR7aO0Gbx8FFJxUCBXffk9b2VJkMvd5xVYtpmmQmB0k7Wkgc7CceP4j37KsJxJT6U9GY5ozP63risRSpjIE/EMNTFKBNRFPEoikOjQRZ7qrc5w/1TdLb6kbtba27/dFIklg8TSgYw54utBQGYmk29fsLjwMx3Aet1v7pJpwPhOL5deaYhkksmiQVimNmysphZKx9plSeu/aJXSipCJnVF1Ssx+H3Y48myfgD6NSumyjJGK6i8z1Zdg6EJiI8e3CSXXaFF5+63FooHcMeHkDvPjW7AWbFHG6bsuP4V3lnF+Y4Bx4lFRrhcecLObm3m2cHQxXnsX1iF46pvThXLkyAJl0cxXHFMfwUHb+/FtPh5aenfoub7onT6XXwkzedu6iCs5x1XV5+dONGLjqpmy/et5+vPNiX706RWXk+oav/B3t4gI4/3gRpyQwnRBU3AleqqhpRVTUCfAi4NPt3Y0wTx+jWki5w9TiHN9Pr34IzM00yEMWW/3yA/eNRdmi7cR55uPri2bFqnvho3e5N4+Gy1/R09a5/82S6MTibjwRKukeapkmmwbFD5R/tHNmCsyiZgGNSq3jP9qEQW47MLOucOcvqUEY3mYpV/y5y46MOT80sGDDLtvqhvknu0+p3uSz2zECQZwas7T80FWcwkODQNGP4ci0D24dCbOr389ThwIzKDIXjYKG795nASv9TrJ14iNCglcRF6bsX58jmimWdg49hCxxckHLYaoxBsycDONPhmr0C4ymdQzM8JgDrOmGaxKrs38loioFg5ZittG4QSqQrnm98nXrpv0UcU3uxR4byjw3TZOdIuOz7n58xbGm9sDNdQ0/gmNgFqSiO8R24990+b3PcKQk/juHNKKkIByfjxBMpBrP7tTwhi6IvbPZRCdDEccMxvJnOO25E9y7nE11f4tNPpLl0wzJ+dOO5rO+ee3eHhdLicvClV53Jdeeu5v+2DPKRO3fnL4Dp1S8gdMU3cYw9S/u971rQSpgQS9RlwHOBjdn/vg88lf27IUoygD14CMdwZQWwnjUT0yWMrKy8rAg8iy1eY+B/fj6v6YMb3TC5e/cYhyZjOPb9AefBe+qXZHIvkWd+iZlJEU5keKhvsuEEDLmb1wcmY6QyBk8cmmLPaJi7d49hmCaRsnFC2liE+/ZOkKkS2cVSesl4sxPH7sNRflcfZpURsi6ldkUylEjz+KGpquUF8sFQuVwlvLhyCVYvDlt0tOp7DvvjbB8Ml+z7VKb+tpomKEYGe6TBMcl6CqXsGCtunAvE0kxGU+zc38dj+wrTDOhG7WB0Qehp7BO7an7XimkSLprKQolNVLzfFh3DOfbsghQvF9S6Bh7Btf8P+edbBh9izeQ8JIstOyRdB+/B1ffHigAeYDCQYCpS+d1sPhzg8YP+iucbZQ8daXjZ/eNRBvxxNvUXBfdlQYxumNNnvZzu9Ww9R8HEHjiQe1PD5azHOfwU9vAgZjpGOmNUfO5oNrNmRjcWfOJzCdDEccExsoWOO99MytfLmzOf5LZ98I6L1/Mf15yxJCZqtNsUbnnxKfzzizbw4L4J3v7LbfkfytTJVxG59DO4D95NyyOfXvCLhhBLiaZp/Zqm7c/9B/iBePbvmalTiV8o2likKFOgtX6lzjl+4ujddId25Sv4B6di7BwO0zdSP9vbxMAeBgMJRgNhtLEI8ZRetZXOMbKlqFJU6uBkjB0jIYLxTD7rYkavLOtwyKq06UUBjy3Yj5II8HDfJHtGSlsqnXply6UvWT3AKVHl+zJNsyT4iaV07t49RrhOK4M2FiEUzxCeGsIWHqp4PaNblc6KQCpRPXCz+/twDj5e9bXh7N368qQo01kW3oln9CmUxPQtX86hTbiOPFxyQ69iT5kmjiOP0jZayLy5ZyzC5v5AzWQi+aQt83SeOKb24JjaO/tsqhWtPpXHoj+WqhowpHWDZweDdYPj3Fba4hMoRlGgqOQ+ew4tV9XWl4mjGBkmo9nPNQ0wjboBTyY4RGd4b6NraGipcCKDERyoeH44O4drIq3n6yFTh7dbAU32XP+LNs6zg/Wnu7AH+qqWyNV/P65D91V/U9E+KN4fiXT161gFQ7dazjLWdSuZMbPrL3yWQuEYDyczzFdQWIsEaOKY5xh7lo4730TS1c1roh9he9jHl19zJn9/4bp8Fqal4obz1vDFa85g/0SUv/u/rfRn79Amzv57Yuf8I77tP8L77K1NLqUQx5j8D36N60U6zgr/Flpjjd9tno6SimALDXBo0gqwAMYiSQLx+pU+xTRpjx3OX9tyJY+nqlc0TUyGggnC2VYuwzTzFRolXdlyZQ8dwTG2reb6c0GXzUixfuQubLHSzI2xlF610uscfQbX4Qfzj4srVfmAtKji5UuO4UoXBUAN3pjaNx7j/r2FlpbcesaqtD6Uaxl+HOfwk1Vfu2fPOA/sm+DRg9bkv3Z/H8vGHsGXGKm6fCNmMr+WQ09YR2eVrmjl8pMOmwaxlM6jB6dIVQmkAZyZQnCcC8xywWMkmSnpPldvKoTRcNJaVp9BC1w26DEMg7t3jzE86QfTbPwepGJDN818oGQLHSGa0hmPpNhyJMBAIM6T/QEOZn9HQ6MHyISswH84lGQklCzJFLp/PMr9RXOU5s+xsvKUXyXSulFzrrPZyAXx60fvqdlSp8SnsAcO0Ot/ms5o9RsqDamysx87OMWuw4211o5nz6vcdcGuJxgN1U+fXzxfoZKOoRjp/PNKqrzbuLW33fvvxBaqDBob7a5riwxiDw/mW2tt+bGMpcdbKhu47RqJSAuaEHPhGN9Bxx03Ere3cXXow0woy7j1+o1ccvKyZhdt1i57Tg/fvfZs4imdm3++le1D1sUsevEnSG54BS2Pfhal7y9NLqUQi5OmaZ/QNO2ymb0r+0NcdkOnbyLKpkN+nCNb8CXH6QlNP6lxW6wfV3r6CZNd/Q/gHHkq/zgRj3Ko/wCH61SCiyv0ZpXnqomnDCYiqfxcS7lN9KQmaR+4D1tooKSlq1wspZe0kuVSvruzwZOj6G44kJ82RDEyOILZsUFVumbHMwZ9RZVjE7D7C42erfFhVk8Wt0IVytAd3g1QmBA8uw8M02QiYrXe3b17rGSsjDLLu+GKkaY1VmjhiSQyRFMZlKRVKVwRsOYz65uIsnMkXBKcKsmQlfSkilTG5MHt+xkJVJ/aoG8imt+XxSUPxmcQACkKh6ZiRBKZfGKRagzTSs2eO5b2j0fxx1I8emCqbvc5+8QulOzUCqOhJE9rfbj7/oQtMlzzPdWkMiZ2PU581x+xT+2Z0Xt3DoU54o8TiKWxxcboG48yHEwwEUnlb3xEssf+8K5HGNh+L5gmznC/1TpV9Fl9E9GS7qq5c6Val8Ni24dD7BgKl7Q8epPjUKe103nkIZaPPZr9/NqcmdLjY+eI1bXYdeShujdSppPWjbrHRCJWWK9uVGk9zpY6d8y4pnZjn9rLCeMP0hnZ13A5vP33snqqzvyJRTck7KH+ojVny5mucmPKNHEObarsEltMyfVWMEhkCtdGPbs9EWlBE2L27BO76Lj9emJ4eVngw9g61/KjG8/llOX1s2ktBWeuaucHN2ykzePgHb/aZk1WqdgIXf5VMj1nYP/9W2t2QxJCzFZpgLZ/PEognsYoDjCmaSFYFtrN6snHpl9VWUvI3i330BarvENcj81IlwRP1QKtfCUz+++uYavFxJUOYZgmtqS/bpCXmyjXGxti/chdKHpZq1uN93ZH9hDt30LcPwRG9VbBaKJoH5igVBuLVraeaEqnPWpV1MITueDHqqTtG4/SGenLd0cbj6QYCZfezS+f00pJR3EmancPXRbeRU9oF+7UVHlRCBSN18qNz9k2VAjOXf334xzZkn+spwstpFOxFKsnHyfevwnFSNMRPYCSsIKhiUiS/ePR/M25otLmu4/WU6uLbEt8qOjmgZlfdnh8nP3jUYLZ8V7+WJon+2sHF7ph8lT/FOb4HlyDhWPdk7beo8Snar21RoEVHLr1PdmqzE9XbR6ycsmyLqNd4dJAL3eMJ1MGtvAArRPb6Ige4Ii/+g2RI/54Uatz+blVep3IBQnhZCYfpPX6t+A6/EDN8triUzjT1bvIepO1E8YM1ChvMmPUvdFS7sBEjGcGgoSSmZpjL3OeOOTngX0TFc9tGwzlb9g4p/ZaiT0AX6rxhDdQGYRuGwoxUa3boqmDnpq+YctIY4sM4xzeBFg3mfaNhUsCbdNupf23Gen8zSvdMLPj0nILSYAmxIzZp/bReccNRAwnLw99mNXrTuV7153Dirba8wctNSd0efnBDRs5eZmPW27fye+3DYPTS+hlt4Jip/1P/9Bw1jkhBCg7f1ORPMExsmXaLo5PFyeJmFXyiukHzjv1QsWrkdYed/9fWDd2H+5UoYVDG7aCrR3DoYpxRHajqEuh1VcOj8OO3V80HqRO97mWbPDoSEeyW1S/+7jNSDEcTPDUoQn+UiVDYbIkE1wjFSETE7MkW2R7tN9qhUvHeHYoSDJbUe6M9uU/dSpaCA7HIkl2DIUrKqQ9E5tKHivJII6JHUBhv9mK9k0yYwBKvnLa6S3MTVZzbNnEXvQ9d2LPfs+5SqEZGuLEsfvoCu/FdfivABUZKosPHZueYP3IXbhTUzNO6LE8uK3qzYO2gQenfW9xV0DDMJmKJhmt0wJTixIdAz2FNhZhc39p61w6Y+CPptg7Xvq71kj31HId0UN0RvayfqRyMuRwLEYklcFu1K7s7xopdLUbCSXYORxm92g4G7SVvsmWiYFpsG0wxOP7hhuu2Nc6g1rj03cv1E0zPwQC4MF9E2w+7AfTYHlgKyeO3s3KqU013x/Idl3dPhhk13D9bLS5a0kibbByahMt8aHGNjEdw9VfCFL3jEbYMWzdIDgSiDMQiFc983XdYCiQKBlvGUvpjIwO4e77U9VV3auNE4yniaYy5Mfy6mmiW3/J0zu2MRJK5rM0JjJ6SW8Jd8qPLzHCSNnNj9m2ujdKAjRxzLGFB+m440aiaZNrIh/hnDPO5iuvOXNJJAOZqW6fi+9cew4vOLGLz9+7j+8/1o/ethb9tT/EHuij7S/vnf9sZ0Icw+zBQyWPleBh8hUuI41jfEfdVrJQvDKBghKbwL3392zrH6vyDstsfuptwUMVWdLyktmWsKJU/66hx4kkdQYDifxA/Vw9xFbUCuh12ksrh9mFeoJlXaYylXfr66VYN0pShBetITd1SFFglAtQan1Ozv6JKCPhJA/sHauoQIHVCqcbEClqjcsFmrtHiiuehbFKGaP6NTO3r1yHH8Tp78NW3vKXvdbuGSsNIBz2wrbmAliAeHEXy7DV7c+hl+7T9DQZHNHTtMSOkDuCXNlusZ3RA2zuDzAYiOTH8NTbpornZ3hE7hsr64ppmqQNs2r3v0gyw73aOHfvLjsf9DSuwcdwDm3i0GQM3TCzrT6FQvZNxiq6+dbKNjpdL5LOiPW6zUiXlHLbgD9/HtuMFE7td9a5li9nElvReZcb33l4Ks5jB6cYCBS1yho6K0cfpCe0HZueZN34A6z0l45j9CVG6k6TY0v4S7rjectaoCr2sGkSjKfzLZ45wXgGV2KClsQIimniSdXunlr1sGgg6vKk/CwPbqs4fkoeZT8nl8k0nT3vUxmDwey+80fTJTdPoNBKmTtHHJO7CcTTmJjsH48yEU5R/Yizbho8s7ePTXv6S54f8Cdoi+d6JihEUhn2jkbzQb+CyaqpTfmuyqUkQBOiYUp8io47biQVD3Ft9IO8YOP5fPLKU+tO0LnU+Vx2vvzqM7n6zF6+93g//3bvPtInXEL04k/iPng3vqe+3uwiCrF0FFVCci0q6ewYhGRkip07niY1tK2kBaq4y9iT/aXdt0zTJDGqkdYNglO1Mw/WrfuYJjajuLJl4sxEcI5uhYHN3L17rKIrVq6lpqMoQYCrqEJmMxK49/6+7jqHQwmrupOxKqPudOGOtT1wAPeBu3GmS++uZ3QTxdRZEcxVaAobdnAqlk9wULwtOeWBTc6y0E5ihwvj8YoDsVhSZyyUJKOblfO/ZSnlUUjRY5uepKVOi0S0KO1/votYlS9rWWgH60fvyW9S8RLFAWXv+EOA1YVx31iU4GznqDIN3Ck/h3Y+StvkNjypACndIJFLBmMaeJPjrBy4mxPHCpnvSrI81jnoWuOF7rT+OuPa3GWV/FziFoceJxzPVATNacNkKBjHqNptziq7ksrePMDM7/vywLWY064QTWR4djBUcoPAMVnoxlivhXrZyF9LdkV3UdbDzsh+dgyFrXMty933Z9aNWy0/vf7CcZmTLB73lL0Z4E1O5Ftbc4FRbp0rAltxH7LGjY+EEvmgA6wAMd33AK6BR/IfaSsbs/nYM4VpBJYFt9OSqMw0mltZ+flnnb+V+6ZaAjXHRNEY23yXYuu61B49UBK0FnOVddUsDt7CiTS7h8MMFScOKVp38XV253C45HwJRqMcnrKmpShR47teNfWklVSlbNuKWyDFA+wAACAASURBVPtz50/5nGeAlQ0z+9mt8QFskdknAGrEsdekII5bSipC+x/ejBE8wlsSH+H88y/mPX9zUuWP8zHIYbfxqStPZUWrix9uOkIopfOZl96EY3w7LU/+J5meM0mddEWziynEopbKGAz5x1jpsbrA+bPjgZIZg5aix/54iomRcRTDwLQ5Kz6nZCwD1sD9lkQEOgvL2EJHcEztLVkuZ+94hOcsb0HJdcWpMu+Zkm2tSaWsis1AMM7aTk/+9cLd5kLFJ54yODIUwp2awpuJgbv6nfLs8H6iSZ1IUicYtgInm5kBsmMzolblxKlHSTvbKP4kX2I0H1Da4xO4UyuAwpiytlg/LQkrWF0ReJaM3U24y06t3n92I40vcph03IEdmKjapa12Bbxed8vewNO400Fs7vVUa7Mr7jI5GEjQsdJJxjCZiCahsLtxZCunHZH9BM0NHIwX3jcVTWPTkyVTA+TGJSUzZv5jZvJT1RXZR0f0IDGbk1y+ueKWJAWTXv+WkvfYIsM4h6xubSZm3bFbdqNQ2U6kDDypSRKu0uRaLfEhlge3YQv5ADfu1BSrpp4k5FtHezZxSiiRYVW7tbwrHWT/eAS6q6zQyNA/GeeEtI4XWD9yF7rNidnqwjW1i+XB2uMADQPGIwnAZPtwiKlomstO6cZf1MWybfBB6Ki2YojHo/xFG2d9lddyLdDbBkOwsvJ1b7LQsrXCv4WxrvPzjzuiB7BFCm+q2ypp6jhGt3LkUIC4ezmnJa3z1pmJMhVJkUzrbOhpyX/HvVOFVrjlRS3bbfFBbGaGjLt0XXYjgW734k6XBtRrJh/F1tKOickRf5xuh47PZa96LJqT+zA6T2bt+ANk7D7GI1Y3ZXe3n+7wXryp6t/R6snHYVVbyXOGaWLHJJo9DyaKbq5sOuSntWi5YuGilnU9Xf3GwXTtWrkskeVj8g774/kW68p0S1ardNy9nKSri/ZYP7QubN1SAjRxbNCTtP35rdjHtvO21Ps56wUv5e0XnXhcBGc5iqLwjktOoqfVzZfu3894KMFXrv4cJ07to+3edxN4wx/Qu05pdjGFWLS0kTDRWIpuRxyny154wcxNjJplmDgO3MuJwGT76ZRWCcySO7qmWWhhsxWN8ypODqGYBs6RZ7DrVgU4kTJI6yYuu4JdT2CUB4GmyXTVkFp172g8zolTT+J22WB5a8WneFIT2G2pfGVyKJjgACFwtGIzdOJpHa/TXmUFhe5HhlKoWoxFknQnducfr5zaVNG1yqEnCcQrA91ySnSUWI0gzpes3X30wGQMlNaqr+WyTXpiQxR30jMx8wFyTq4LZj5BhIcKXZH9pB1tjCVTFFdJV089bqXCd1m9OSKpwrQG+e2ruQUFuSQjucDBXqf7YrHx/p2c4jsCDjtH/HH8sTSTNj9dbb7Khat0i185tZnBnotJOwpb5dStPeYc2YIzfVb+ZoCvKIlFMmPkb1i0JEZJA8FomuKdY9cTuPf/gZFAD4lYlLNXt+e3zcSVb8GtJ5kxsBl6vltc/8HdmEWtd7ZUGMVoq/redKb2uVSvG2A5X3I8n0EUoCu8F4as8VjW91S6Ht2k5DyyBw/RHgvRHjtMwAY+Z+EaFE3qpHWD0ezNCW+qXqIVpWL+wRPG/8pAz6X5bp3FHHqMAb9CXzzCbv8Qzzu5t+qn7hoOMxXZQbeexKEnGc5fzqx1eZOFAM2ul7bUWS2YSn75e/eMc1WvwXik8rstLnn5XHLTTdqeSBsEBnahmMtwZiKknB0Vy9hiYwTiaQbLWhOLuxPrdToYdkQP4EqHgfa6ZZmrY7fflzh+GDpt974X98DDfDj9Vk554Wt5x8Xrj6vgrNgbNq7mm9efy97xKH//6z3sveQbYHfR/ue3Qqp6ymYhBKRrjD3yjlh3q8ertNwsC+2ueK6Yaej5CmutNPxKbBx7qJ9loV2F92VrKaunHqsRbZkkM3rNpBO1ss+VXxUPTpZmRuwJ7aJr7In8/FdWdy2TjoiV4n7fWLQi86EjE8OTbUko3gbI3RkvlH8mFd5yJlY2xGp6gjvKFi7sl/KEKNVa1KxWCus9ad0Kskta6oo+T6/bH7V6MhVHWYU115VqLJQkEM11/Zz+N6uyi2h15fu5O7wnP8FxrtIbrTHh9PrRe+iIHqx43l3UVW39yF10RqyWZt0w6YrszbfklHRHNGtP2OxO+VkW3M4J4w8C0JIcBjM3CbAlmZl+XreJSIpotkuazUjhSU5g6KXbFkvpDAzWnvDaXqcLZY4rHcSbHKueQTArl0E0Z/dIJH/0lx8XoXiadePVJ16udiQMhRJMNZAQxVSUqq3MtVq4wPqOVk5tZt34A2w+aJ3LznS4ItCqrrK0vrJMk4dLrkcmipEmeOjpqkPki+feKx+LGklUP2Zz9o1FmTrwND3BbayefBxfYqRkGgwAe/Agh/1x9GwQq2BWXD/i2eNpWZVrdle2C2wgnp52Xsq5kABNLG2mScvDn8TT9wc+n76Rngvfws0XntjsUjXdS8/o5VuvP4tAPM1b7hhn5/P/y0oa8sAtC54aVohjUSKj5+MMW5107+UVcVuodqUwJ9fCVtwFyjBNDk7FsFdJSOJNTaGYJtpolL5Ja32heKahbHbeopamWEqv2XqQ64II0B3aTVekMAfZWChVdB0xWTvx0LTrnU5u7FB7lcAgZyaXrvx4MKBW4FNeccvJJe8oHntV+nnTm0mSjYGn/8yhyZndPJsuS2Yt0aKxNa5MCG+ojxPG769Yrloq/rYa+2vncLiiQl4sVm0uKmDV1CbasuP/TEyU7CoPThTOrwF/Am208WzEvf4trPQ/RSBa2jIzEUkRqBEogtXCVK583Nvqycfp9T/NUKCxILncqqnS5CCDgUS+K3Awkba6UWYp2f+ZRTeag7H6wUlOa7z6PHPlN09qyQWSayYfzXdVrWdVnWyQOcWBlkNPcOLYfVVvdNj1OJ7U9BNLQ/0uwbmbCSsCWyumwSi/kFS7aWQqtmxZa7feHp6K152Xcq4kQBNLmnfrd/Ht+B++m3kF0Y1v4+9fsK7ZRVo0zlnTwfevPweHTeG6B33sUd+DZ/+deJ+9tdlFE2LRSxuFTA+H/XH2jhYq0IHJ8gpQ4Qe/vLLiHHuWWsKJ7B1YU0cbi5R8zr6xKOF8FrZ6lf1CLWXrQPV5k4oVB5C1WqPKVetOpWcTFXRF9la8Vq6RUCKXCa87rDVUprlR8CQn8vNylRsJzjw9fKmyLqjTZNItn+epnvUjd80+M69S2iK1cmozrYE9VW8C1NPr3zy79U+rkSyBARx19pdTzwZzRyF7cUu8SjKOqqbfrvKWGN2gIoPlUaMotRN+ZMrn3mtcv98KvHPBf7XELbW67LrrdumsZDNLP6c4OFYaOt6r73fzKPbMkgBNLFmu/X+g9bHP8Qf9Bew89X28729PPm67NdZy8rIWfnjDRtZ0erh62wUc7rmMlsc+lx8oLoSolDEMdg+H813ekmUtAOU9IeuNAwrW6QJzcLJwBzajmzjKJ3mua3Yt4V2RfbN6X7lnBoIcnIzhrJJqf3lZOn5Xuv48SjmzbRVq5HPb4kfyj33JEVb6n6K14Qp2QXnWxeJxNznlrWfdRZMix2u0JuWUt7IUy3Vb9aYm62Y1rEUBag3hqVUhr2AaVbe5Hr2sG64nNVnSjQ0glmo8oFo78XCdV2sn1pmpeq2CUHmcz0W11rHEDPbJfGupMlYNmNU5k1O+jdWyrlabhw+gJTGzjInlmS6LxdP6bC+fR5UEaGJJcoxsoeXe97LFeA63r/s4H73yNAnOaljR5uZ7153DxjUdvGLgTQRcq2m7+x35OUiEEKWS0809Vaa8W2OxwRrdoXJ3k6HQ7UypUWmwVRnTVO25zhkEX/PRwBCeZjzITNkWqNXDlYmUdFWq930Vi5dXkE3on4wTy3XXqlHe4sx+QGk3sRrfsTs1VWMGp4LcWK5e/xZcmca7/eWMh1OM1BjDtnrq8YY+o9pxN53y+bjc6RBrJh4pea5vPNrw91JPLsiv1kWzWeZWlObUazJT/dMvdBRV62pZflw1qmLOvhmRFjQharIFD+G74yYG9U6+veKz/MvVG3HYJDirp9Xt4OuvPYsL1RO5LvQu9HiItrvfDvrCDXAVYqmaexe36RXfTc6lclfM6hWOXBKFYrkU6rkKqTvlzydtaMR02dCm04wK8IHJmbQw1jZfZV8Wrp70Zbq7/Yemqm/HXItVnnWyEZPZcYuNBEfudAi7MfcgaiHlWi+LW0ybLVN1zrfFa/Xk4xXdXveNz/yGwEKb7xtEjeg6Kl2wLRKgiSVFSfjx/O5NxFJpPtf+GT75mhfidshh3AiXw8bnXnEazzvvQm5J/gOu4c14Hv1cs4slhMhqNGV6CUXBmQ43NFB/qdP1xVXRbUnUTu3vqTNmJlTjzn9imu6P0wklF/6G25qJRxd8HXORS7pROrF7k83hsK3V5W8hVes+W9GafIyz1bhZVn96g3kuw1FbkxBzpSdx3n4TzsgAn/J+jA+94UpaXDKV30zYFIX3X7aBky59Ez/KXEnb9h9g7Pxts4slhJglT2qKNZPNqDQvrmBpsamX/a2WRlPo19Jolj8hRH2NjptdSBKgiaXBNLH9+X10TGzhc4538fZrr6PTO/3EpqK6Nz1vLe6XfJanDJX2Bz9E4Mj2ZhdJCDELzRpr407PPpubEEKI+iRAE0uC+fC/s6z/Tr6p3MCrr3snK9rczS7SknfFmWsIvPTbRE03rjtu5tCwJA0RQgghhGg2CdDEopfZ+jNWbP8WvzFfxLmv+xQndHmbXaRjxjmqytCLvsEaRgj85h08c6SxCSKFEEIIIcTCkABNLGqZAw+y/NGP84hxFh3XfIVTe9uaXaRjzqozX8zYeR/iCuVJnvndv3H/3vpzvwghhBBCiIUjAZpYtDKju2m/6x/pM1YTuPI7nLOup9lFOmY5L3wn4fUv4xb7bdz+x1/zy2cGm10kIYQQQojjkgRoYlHSw6M4fvtGooaL3X/zXV5w6onNLtKxTVFIXvEVjM6T+I7nm/z0/if51sMHMRfRZJ9CCCGEEMcDCdDEopNJxojddiMtepDHzvsGF59zdrOLdFwwXa2EX3YrrfYMt3X+N//75EE+c5dGRj++5j8RQgghhGgmCdDEomIYOqM/v5n1yb3cp/4rF1/0omYX6biidz+H8Iv/i/WJXdx2wu/5464x3v+7nURTMr+OEEIIIcTRIAGaWDRM02T3Lz7MxujD3Lf2XVx4xY3NLtJxKXXK1cQ2vo3zx3/LT8/axebDft7xy21MRlPNLpoQQgghxDFPAjSxaDx5x9e5bOo2nuh6Nedc88FmF+e4Fn3hR0md8Ldc0vfv/OTSOAcnY9z8860c9sebXTQhhBBCiGOaBGhiUXjwnl9x1ZH/YpfvAk6+7isoNjk0m8rmIHTlt9E71nPRsx/gJy/vIJrSufnnW9k5HGp26YQQQgghjllSCxZN98cHH+SqvR9j2LWeZTf8BMXubHaRBGC6Owi+4segKJy/+d38+HUn0+Ky8/ZfbuORA5PNLp4QQgghxDFJAjTRVL965BlesuN9ZBw+XNffhs0jE1EvJkbHekIv+z72YD9nbHo/P7juuZy0zMctv9/JHdtHml08IYQQQohjjgRoomluvX8nFz/zXnpsETKv+Sm29tXNLpKoIr36QiJ/+wVcRx7ixGc+z3euPYfnn9jFv96zl1sf75e50sRxo8snrftCLGYJV1eziyDKuJ0SasyGo9kFEMenHz9xkLOe/GfOsh8icNWt0CtznS1miTOux+7fh2/rd+nu3MBXXn0Tn7tnL999rJ/xSIoPXX4KdpvS7GIKUZWqqhuArwKXAFHgF8DHNU1LzORz1nZ58MfSC1DCmduwvIW+8WiziwFAxu7Ghd7sYixeCjDL+1i63YVdXzwZdN1OGyta3RzxxzEVBWUR3aDzumyM+dbhSfmbXRRRJGMsnmNkKZGwVhxVpmny/ccO0bvps1xl30zk4n9BP/mlzS6WaED0hR8jedKVtD78L/gO/ol/uUrl715wAr/dNsyH79hFIi0VNLH4qKrqAu4EksBFwBuBVwOfn+lnKSyemxCL6X7IaNfzm12Eo25Vh6fxhedQPx3tPH/2b14ADpuSb0k2lMV1j//kZS3EPKuIeFextmsG30+RpLN9nku1dBg2+4J8rj7LAC3u7pnV+85e087Za5b+9ygBmjhqTNPka389iOvJr/EWx71kLnw3yY03N7tYolE2O6GXfpPMyvNpv/c9uIYe558uOYkPvvgUHuqb5J9+tZ1AfHG0LghR5ALgFOAmTdN2a5r2V+CTWIFaVTH38orn1nV7AbDbZx8ZeV1z/8lNuLqqduMa6zy34c+Y7y5HhlKo2CVcXQ3tI/sSr324HNW3MWOvEhjMIZhOOTtm/+YFUFLVVko3bEZB6wJKOrvwOmcXbEy2nwlAtUTSx3pXvUDLKZjK/N/5me1HjnaeR8S7atbrXerJwJd48cVSoRsmX/jLPjJbf8otzl8RV1+P+eJPN7tYYqYcXoKv+BF6x4m0/+lm7BO7uPbc1fz7NWegjYX5h59vZSg4o15jQiw0DXi5pmmRoudMwF3rDUlnZ80PO2Nl64wLcFKPj1N7Wzi5p2XG760m5l6BrazW0+IurZDWqkyu6/bS21Zz0+cs7WityMQb9aysWM62mJoAG5Cxu1nRXthv5Xu3u9UFwETHWYR9a0tea3FVDxbiru55LePRFvStL3nsKTvmptpOrfo+p6P6sdnVMvsxnmeuas8HjzU+viGmUvvNsw36lkqg4HLYSM7mhsA0p7JrFndjop5eUGzQ+9yZl+coOBrjkZfIYSOWsoxu8C9/3kN4xx/5gvMHJNddRuRFX5r9bRXRVKani+Arf4bpbKHjzjdjCx3mxc/p4ZuvP5upWJqbf74VbSwy/QcJcRRomjauadpfco9VVbUB7wIervWeVJVuTrnLlYJSEQxNp83twOOwY5+na57etQF3WS30uatKy+ysUSmq9fxMpR3eohaTwnZl7F7S7u6SSttk2+lVPqF0X3hm2bp4Uo+P56rqrN47E0lnJ3ZFwZXb74rCRLa1BWBtUetRxuYtfXONr123zyBQPko/l4GWk/N/j3fUHxuecJV2QSuOuROuTnRb9Ra1ZTUCsbGkg6SzkMm5PNCt5cjyy3A77fldtBDjoTu8s+/OWXzO+VtPyf8d9aykf8XlcyrXTBxaeVXd109bMb9ZtHvaXKxf5uXknhbWnvOSho/hsHcNk21nAPDclYuzq+IJXd7pF5qjxdWBWBxzEmmdj/9xD5EDj/MLzzfQl59F6Mrvgsx1tqQZbWsIvvJndP7udXTefgOB1/yac9eu4vvXn8N7frOdt/3iWb54zRlccKJk1BKLzpeBc4GKgVOKAi6PA6V7Da74syWveb0ufD6rleQsn4unjwSmXVG05QSWJQfxtRQq4i7P3H52TZeD5as78U256enwEEpmAOjs8JZ8ttvtoFpqCZ/PRVo3cM0x2cnY6su51LGJyWQGb4sbJaLg8jhIL1fJ2GP0jgWIZMvma3HhCpdut8dhQ8kYDK28nPZwH6d6xjg0GZtxOXq7W6DVPef9Ws3QypewesSK7XWPE4/HydnLWoimM+i9GzGm2nGlNAB8LVYZvD4nyU4VV+Zg/nM8bgfpKrXT9HIV19g4AIqiVN0GX4sLl8eBw6bkky0MrL6StUN3z/v2AqRWPpfWkQFSuoHZs56wz8Uy/9aSbfFecCNj24bwpkO4og7cDhvJjIHP68blyR51TjtpnxtX0sHpK9vYPRLOf4bb48SVtMYsBzrOoDO4i+Wtbp5pfxEoNlqih+kK7MDjdZJxr8MXH6pbZnd7OzabQlubh/OX97DGSMHwEC5Pcsbb7/VV7m8Aj8dpXR8Mk1a3I39sA9gVBb1OspQ2jwMzYS2fWf4cdEeMuHcVcU8vXpsDV6j+sTu6/GJ6xx+d8baU56bJHUsVy2WPvdXLWxgYsuOyNXYutbgcRFOZ/PdfzutxsrLLB0D7yafQ5d+CNhauWK6YbvcQX3k+udC+s8NLR5ubeFon6erC3UACmNz11uVyUFkqK4BvdFzcynYPI6FCr6CMowVHJpo/3xeSBGhiwQTjaT7w+53EhndxZ8uXUVrWELj6f8A1P918RHPpy04j+Mr/peP26+m44wYCr/kNG3qW8cMbz+W9v93Oe3+7g09fpXLl6SuaXVQhUFVVwcrk+E/A6zVN21m+zFj3BcTiOsloilS2QmW3gW7AeOcFuPyb8svmXq8n4G7juWe/gsj4Nmyx8YbfV0/SyNDrthOLJlnd6mIi26U4FEqUfHYCk1SiMnGPnsoQT2VqlsNhV8jo01deYtE0MUeSVCJDLJbGNM3832etX0H4cJpUthIei6Ur1qe4bDxnWQt7EwoRx8mczyg9HgdDgQQT7WfSE6r4emqUI4mhlG57u9dBLKU3tB21TLSfQSRR+L4SZEiYaVJ2Bbuniwn7KmKxQP71WDS3L3SSmcL+PaHLy1Q8VfW7CKY8dGWXc3kchHUf7nSobPtSDLlOxeby0j2xxXouptc9jsY6N9IR7cOdDjPadR69/qfzry1vczEerp0VMhZNMeo6CSMZ49QuL/smrHMhl00yYZgEAjFi0RTOtPW92lw2UimDRKJw3sRMG/G4tR/MdGl5k+7C8TDWuRpfYhtxh42YPQWKDTNppyWRIex0E2pZz3r/YZwOG+kqQUCuzEa7SSyapGeNjWRmGXrCYNR5Mj2xfejVjoMamTXjsdz2KiXvS2QbwVKJDC63vWR72rwO4nW+D8Ou5JePxk2inrOtdccNwFqf12Wjt83Nock4KUcrSWcHa5VRgrEMsXjp+Rr2rqEtPlhzfQAblvtw2W3sHin0ZsltWzmXx0EqkSESSZJMZCDd2DWqt60FfyiBx+uo+rlxp41Y1AqSk4EYKbOFVKJ+gJV02ohFC8enP5Sgw2ljPOFiyHse60PVb0wUZxTNrzOVwaxyyPS2uxkNNRa8J1yl1y5/Wy9dib35830hW7ali6NYECOhBG+97Vkio/u4ve2LOF1egtf8L6Z3WbOLJuZRpncjoat/jD08QMcdN6IkAvS2ufn+dRs5e3U7n/jTHn721ECziymOc9lujT8E3gFcp2na7dWWS3p6SGYTcHT4HKzt8uBzW/cxTbtrdit3t2HM4Lq3rts7bQa6Rnpwxd29VZ931HnzZPsZdcfZpB2+woOS7pqln9nprd5DosVtzydbAetOdk+ri9NWtqOg0NNi7eOI7wRGu85nqq2xroumo/CZqzecxeqzr6Bthne3R7qeV/f1qbbT8n+n1/1tfouLy7i8zZU/fnJmMlZleNlFJY9zXdIivnWYrY0nS4h5VpL7TnRbofXW57Kzqn36RB6RtlOYbD+T9qJ9mHDWHi9X3HiUdngZ69zIRMfZNRNOTK57RY1PspaPu5cz3H0Bp5++keeutrrdOWfSbdHhJXrSy0k52nDUSFhTPl4uJ5eZslb307B3Tcljp6Nw3E6np9XFi55TPTOhgoKiKAwtu4ihZRcDhfNIV0o/f6pql+FSLS4HTrsNo6g17ORlLQ2Mh7O+zNGu8zi84nLcRV2PR7vOK1nSsHtKumwWSzo7MMta4uIrrERGUc8Mbtra3bSsv4CzLrwKFAVfjfGcue60p/YWGgBa3dWvAbnDMlP2HVdLDlVNLtnTST0+Tuud+ZjkRkmAJubd/vEoN/98K/bIAH9o/yIem0HwVT/HaF/X7KKJBZBefSHBl92KY2ofHXe+CSUZpM3j4OuvO4uXnNrD1/56gK882IexiObLEced/wJuBF6radpvG3mD1+mg2+dibOWLOLTyKkxb4xXt3nY3Jy7z8oL1tZON1NLe3VuRAKScUuV1xeEqScbh///s3Xd8I+Wd+PHPjHp1L2t7i7cNu2yjLL0sJVkg9COFcCmXI7k0cimk/Ui7XAqpl3ZJSDuSkEAKkAQIoQcCoewuLEtZZnuz173ItmTV+f0xki3Jktwt2/t9v16GtTQzeuaxpHm+8zzP9ynPn9Ux39ENRc1olGdLJRDo9SzJeDw9i+PJi8xzHgoEFHAlG1X1FX7Uysyg66SFpSwuHw78UoFQyFFFwNOYtyxDZVp+GYbFybp6P43aBnyLToACQ7RSyTyynXZc5jmFshpr8VzZGSGjjLn+bIbFxgLf2LMbRhdkNoJTAd7KqsIjT7ITcrSXbCDgXkTEmjaHZ4wxzkkLS2iscI8I1lvKTqat+uy0w5nf6bV+BygMzc8LOmtJqLacL6gtKMUYQ0HC9nJKPXY8tuQNkgmuVZD3vZ727/SsqHGLi5byjbRVnpl1nNxHWl7pweewUlea/2+cei2nTR2ew5hHxOY330hpb6ZE1ndPduCT93UtNg5VXzimbYfnk5qljasOEqoNrdrP0io3y6s8hBzVQ4lt6kudoFjo9S5HYWTil46SNSN6KFW7jwO1FxGyjy0QSkmULAarizOXlrO4vj7nH/Vo+SlYLApO6/B7tjaZDMmWlXHVqiosKnexrNKd8Xhb2cglLbprz6bHu3TE42DOLZ5IApSxkgBNTKlth3t49++2U250c4//GzgSQXov/y3x8tzZnMT8EF20icBFP8ba8UqyJ60bh1Xly5eu4i0n1vPbbU185r7XiOQZoiLEdNE07TTgw8Dnga2aptWmfgrt58tOBDKOhA41PgclThuOocZC4cZlIi1zXLxsBYNVG0Zs0++qAyCQlTkvxXBV0OlfPfS7z5X/rn56cyX9rnFcdWQEaKlGW8SWmTwgo9EPZrY1zMyRFcneBI/d7C3LvsMcqVhNIYGs4C9dtd8+soGbVncue+GG66JyV94GuyOtEXeg5vXELWavnNthoTRHL1iunsZcjXjD5hlx139tvY/yZKKM9Kx5P41CdAAAIABJREFUqqqwRjs+c/+059IdrTyD5orThn4fcNZlPB+zuunyr85o6I+1D8pjt7KyemTPwKCjkqht+PFUD1mJ08a6Or+ZBCf592iscGcEYosrXNSVOrFbVRaWjkywoABVaVkyF5dPTxKG2pLk/KS0rqSW8lMzthm0V4wIioZkReGpBCAVHtukEolkB/d9rvQb2jnmL1pz1097ydqhf8dG+aylq8px46LW7wAUvHYrbruF81dWctKiMtbV+6nw2IduuiqKwqocPUnZN7V8TitatRf3KJ/TjGOk/dvrsJJYsol1deY6Z8cvGP5eiltcVHszv6NTdaoqylBPas3C5ZS5bZS6bPidNo6Wn0qX7zhas4KzoKPK/B6w++nxrhy6KTWTt5klQBNT5mG9nRvufImV7hB/8n4NR7iD3kt/RaxqdqZJFVMr0vh6Ahf/FGvHTkr+fC3KYDeqovDRTUv50DmNPKS386G7XsqYXC3EDLgm+f+vAkfTfzRNG9FS0Kq9bF5VPdQATzD2jI2pNbBSwyENW/5ej/SG+dGK04cbXIpK6YLluJeflZF1raNkHQerLyDoNIcuGlYX8dLMO7tG8pJuKApasoGdfmc7O1Ni+vCvPvdCQo6qjAx4qUZbd9WptFaexrq6/BnVAr7ldJWuzXis1GXDblGHGurj6YXUcjT4LMpwADheQUcV9iWZvSKpBl6feyEAS6vc5rCntKDPoio5h4U6cwRoVV47y5JLKbSXrDPXpssxckBBoS4Z/CZUK75kw94wjBEtdSO5v5rVSNcWNwwNX0wtqdBWegIBz+KhbWr9jlEzjq6q9aLV5H+fVid7g1PBWFkyUU6Z20bU6qPTPzzczrB5aCs1ewCXZS0pUeK0UemxYzjLRgTZLeUbGfRkDh08ribZ+Fbyr/EXclQSsY5viFl69tNo7YaM4XbxtPdnnT+zsb8g7ffsP2nCW4uCQvkE35tgBuDqknOHy5a8QTCmFP1pb40BV/3Q3yrhM7Ng5lrOYVWtF21NoWG9yoj3uM2iZgyvTv0737DpaI71GpdUuDlpyfAwz/Tey+NqvRyXdWOg0MCb8WTrXFHlobHSTaXXMXQjJVp7EmF7GQHPkhE95n3uheZ8yBGvP3PZxyVJiJg0wzC4besRvv/EfjbVRrnF+G9sA830XvpLYrUju4zF/BVZciG9l/yckvuvp/RPb6LnijvAVcHbNi6k0mvni3/bxbvveJHvXr2G6mlci0mIFF3XbwRuHOv22XNTzGArgqJAwlWOGuoaei5mcWKND2f46nM1kFBtRJaehBLpx3AkA5ocrYyW8lNQEjESyZ65o+Vn4AseIuGuRlEUFi5cSqRjgFhLWlnSGpCRpZvNf7ySzLCXNSxKVRSWbjgfV+sWDnWH6BuMDa9/ltzOabPg9jsJtA33pGUPr1xT52PFygXmXeijW/PWW8C/EiU2MhFGqtyd/lUsqjsu5/O55BvmWeW1U+W1s6MpMHQuhsNszBv2/AFke8k6UDPDnNRLdPqPBwO8divVXgvps2a9diuReO6ef5tFIZqWSEJVFJZXedjbMcCAK9Wj1THq+ZW5bLQEozkbnKlkc9nVUeayDfWqpdZZCzprhgJ4gPX1JezpGGDor5KjSkdbdkGrr8Z+2EY0bDacU0MtT1lcxkvNAUpr10C7mbAiVnk88YhtuNBZIos2YdjN4/R6luAOtwJmj1VfeQNER75/vA4rce8CyitiNO19afg8DGgdZd4gDPd4hOzlVCw9ESUtG+Ly5cfz+B6zI/1o+anELK6hZCQL/E4q6v1D77PhelKGe1EUMOw+4u4a1P4Wor7FdIUNBu3l1HX+M6Mc5oLL+RNkOCwqVl8Vm1eZI5E6+8y6OH6Bj33x3J8FSzKRSbXPQVuBpBet5acAsAw4XPM66o8+ZA6TrloNZH6mRws/Ep6aoYRHZS4bmtdLrZrnWp48mGHNfD7hraPLp9HvqscdbsMZ6cZhU7FbVCz+MqwBM1HR4nJXzjlnkYVnY+lrwtKzb9SyDzhrccTbsFlUbBaV9HeYYXUDA0O/n7O8gkMtIw5hfrUOZRtRcC4/D3q35D7nKSQ9aGJSovEEX3pwF997Yj/XLE3wk8TnsQZb6L3sNqL1Z4x+ADHvRBefR+8b/g9L735K77oKNWA2dy5eVcN3rl7D0cAg/377dvZPIKW2ENMtX4ILhZFDho5Ubcr43VBUnDUrzDuvjsLr9xiKZSg4A3OeSa93WUZLfDzDaRTMdYfS2fzVBe8yGxg4C8yJiTScRWzJ+aPOicu57+LzMn7vcy/GsLnzbJ2U1nPlc1jyLmicLeGtI7L4PBL+4XWzFvidIxY+VsgMdHLOGUv7d5nblnPoV8qqWjMwPFJ5dt5t0pmN9NwWlDg5NceyJA3J4YDpQyo3r6rGabMMD0dVlLxz5JZVuHGtPJewrQRbsjsm3zy8XAxXBZFF59KbXB8tff7j2jp/xnpQqeALcr93DWfp0PzAbt9xNFWem2MrWFc//NmxqgqbVlTm/VyOVXvJehZlrV2VXsawvYy4xcm6Oj9r6/w5E0yE3Avp9q6ks/51dHuX47CqJDzVGA6zNzzuKCXgWTJyHUUDOkrW073ksoyHU72btSVOFpQ4snYx+3lyDZtNJU6xWxSWnXA+5cdtGkMNJOfxJes/Yc+15ln6d0+y1zvrQ5LRa68oLKlwE1u8iciSC1m+KPe6dbHKzJ51FIWAp5GEah96nVSPf6x6A2c2lnPyotLhXtTs83BVEKsuvEZfSnvJepprX5f3+dScWcg9bDn7fXzW8kqqK4d7AOO+eqaLBGhiwnqCUT7wx5f4y8ut3HiClZv7Po1lsJPey39LtO7U0Q8g5q3ownPouey3qKFOSu+6AkvnawCcuriMW968nmjC4Po7trPl0OhrmggxUy5bVzdiWE9655fhriS88koAludI2hCxlXBiw+iJQdpKT6ByDI3ksYRF7SVraa44HUUhx0LY5u+pIWWpoUi2hafgtKsZySsUw2BTMsNcuddOV+OlGO7KocanaewhY+Z+ows3bia8dHhIZ5nbzvl5Mt6N5fWsqpI110kZCi7CthKOVJ6d2fhN3e1PnuJxNd5R13FM7R2z5h4ieHrj8P7NFWfQ6V9Dvg6rBSWunEHBojIXm1dV50wusaG+lGVVHgq9UxRFYVH9YiqPO4v65LDK9EW1UwoFbYazLHc0mxRZcgGxCg3D4ef4Wh8Oq4pFMXt/llXlDsqzsysqDNf9aAu6L6v0ZMwZOqFh5HvNqqpErF6cVpX2knXJmyGZx82VuMqmJoefJstQmxY4BcqOJ6HaSKj2oTl/cf8iDFc54cbXE/UvHnE8GA7EfFl/39TL+52WHIFY/l4zr906lFLe6inH8NTQUr6Rpsqzcu6TUpv8vDdXnEa4/szcG2V84eXaIL1c5gaGswzD7iW68OyhYd5m92LBA+WmWnDaLGMayhxZfB7NFafTUr5xZNFInoqiFBxaPdYh00r6OVgchFdeSXjllcQWjFhOc8rIEEcxIfs7g3zk7pdp7w/zg3NULn7lBpTYIL1X3EGsen2xiydmgVjdKfRc9UdK7vlXSu/+F3rfcCuxBRvRqr384toNfPjul/ngH1/iA2c18raNDTkz0wlRbEPZ47Len267heOrfZSFnbhsFv6eOIHoGOfDBJ01nNlQQsKAR/R2/C4rgdDIuZmLy930eu109Odft2ogmfZbAepLXHTCiCCgrsSB12HBY7eScJWjlC5iZZVZ1p7knfS4ah9q3zSUOKlaMnLeSmqYj5GWtTEj9f44RWvS5hbZxpcUoraiFLsxjoWIFWVozkxCtQ4FVTV1i6hdVAZkrdOW3pOpmudb6BsqvOxiSGQOhfQ7bcTLVqC2bGVd4wKsVjuVpRr0Hx3eaBzZbSNWL4Np84msFgWH3UJX8r2zvMpDudtMThPKGi5YV1lBrPIqLLv+NOK4cV89DTTRVeB9BuQdlm7YfcQrzLlodSXOofl1VV4H9t7czcyzllaws6WPUCxOIBSj1GWj1u8kMBjLuzSBVuMxF1W2qBl/rmqfA4w6or7h5SzcdgsbGhdQ6rqWF/X2ocdz/Q2zh6pmnLPXMZR8ItUbraoKMaubzsWXsiR1Y8DmxquYf39/VrKQcredcxsrRtz8sVtUgsRzZ4hU8uWNTJ1HYmg7MIeJpqQnZ1mbnDdalxaUR2yloyc9GsPlWElkDUlV1IzvhrE4rbEcR6v5HZRwFr4hks1wlHDSSjeRWILWvjDlgcz3jdVTDiEzYQ2tQ4VM22DkjYqYxYE1HqbEaSVkAEbqI5oMRmUOmpjNHt3dwRf/puOwqvzuvBDrn/tPDJuHnqv+OPQlLQRAvGIVPVf/iZJ7rqP0z2+h74LvEF5xGXUlTm596wn89wM63//Hfl5u6eNzm1fmXbdEiGLLdVluKHXhSCZNiMbzB2fxsuUo0QEsfcOLy6rJgOH8lZWoisLDaY3IFGvybr7LZuHAaOVTlKGskdnzihQUSpw2YtXriHszs/2VLVrPy72uEet35WKoVpZUuGgxrDhtKpGF52PzRamIWggxMtaI1p6MGmxLn+YxRCWZPnscYvYSIARAyerXo4QDuYfS5UjO4nLYMRxmMNnnasDrsBJeeiVD/Z3JBnrCaQZAqWAuWncq9rgLmiN4nQUanxYHqXwyDqs6tA5bwt9A2N9AqvmcCFehpgdoBZRnDdFsHqWHxG5Rh5J45FsvKpdY9YaM92Yum1eNY+2qHDKCccw62pDs+QrHEkPJO/KtE2buM/KcTlls/gWNRaeT6MkcNp9an6yhzEUwEhtxk8Vjt7Ku3k+Vx86Wwz2ZN0lyBM5alRebJczSCjdum2XE8FeHVWXzqmoOdAVJ3Tqo9pvb5Eoqs77eT1t/BEe7mjG8N/ul60qckJwbtcDvIAbEVAcQg1ECorocvaWFKKP1dqWn/3fmGi0wvjyHdos61LMfXZR7yGshfqf5Gan0OlD7snpIF29ic7K8Ece5kIhjCRw0X6vmBFBzzG+z+rHG26n02Elb45ugowZf8MiMruUrrSExZrF4gh/84wC/2XaE42t9/GjNHhb+8xPES5fSe+mvSfjqRj+IOOYk/AvpufpuSu6/Hv+D72OgezfBjR/BbbfwlUtXcfy2Jn7wxD7+rXOAr19+vHm3S4hZosJjp2sgmncB50jDWYABBwulG7MTW7ARS1/TiMVRR0vSAOZcKIuhEM+RfGF1rY/dHVkRUI7GZaxsRcb8kWjdKWAYqKqFuKsS4gaKYiZyIBHNWY5Y1VrcjlLOLl1qDklTFLQF0B+O8UJr/4jtE/4Gc17Y3s6hx3xOK9YVF1LryZ1448SFJRmZ9horXOzvCnGw+nU4qn1UR17FcFeC1YmR4w44ABY74ZVXYmt6GgjQXbGBs5eZDauORZcS7AyyPCtLHxY7kUWbqLJ6WdYTHhoOmPAuoATYXAKOXWMLejYVCDLipUuxtu0Y+j1sLyUVdGbbuGi0oDlrmOBEb+5P8+iFhLuqYDDuGONcQzCDbyU6QNRiBuGpgLSQ45NzBQn3Dh8n+f+8C3crqcyjVpSEGbjZrFa0GjMgGEvgU+m1U7HiDCI553uZQduiMhdh3+bcgVaOv0u5204b0FZ6Egm1l0WWAuc/gb9r2ObHFhsgoVjNmwx9+beNVY9cDiSuOrARNDPKFntUTHoveKp3LhmgZWvIs45dagRFyFHFgdqL0EaZWzyVJEATY9LRH+b/3buTF5oCvGl9LZ/x3Yv/yW8TqTuNwCU/H/d8A3FsMVwV9FxxB76/fwrPlm9j6d5D3wXfQrG6+NeTG1hV4+XT9+zkHb95no+dt4zL19TKkEcxKyyt8FBf4spoREYWnz80r8FwpxrjbaMeK7rgFBSXBUbGMhO2sMw1lKhh5BS0tAZKVoKORFpPWno4Z+S8K55ksRMvWzZi8rrLZsGqqiytHtswxWUN+W/mVWWtZeRz2qj0xDmgWEBRidWdMqbXSFla5YaGsd31NpylWIHllTPTNHLbLfR4VtBX0YixfAn0mYFxU4U5P0grsG86YxzDJAtZVO6iYtH4F1efSfGSJcRLGzmuL4rfMbnEIQUpKuElrwOrE7W/GYz4uAKO5orTsfpVKvyLRt84z5pmKQbQWnYiNd3PDz0WtzgJuUb22pe6bMPlHMP7onb966nu3TKUQbPTv4Y+1yJOX1mPx25FaStwjBw9UG0lG3BH2olZ3URtpZDoLLjcyMwb+TdM7x2u9NrxORwMzILmhwRoYlRbD/Vw0307CUbi3Pz6Bq46/GUcWx9gUPsX+jZ9Lec4XiFGsDjoO//bxMpW4nn6K1h69hG46MckSpZw0sJSfv22E/nC/a/xpQd38+S+Lm56/cpJZ+4SYipk3+EfLUNjPglfHat9MPblY6dOoZtoFW47rX3hCWVrBHNuzsVraunpmX2ZWb12K1GrmpqxMzT0L3s5hWLwO60EI3Hirgqw2AAzQIvacve4jKBMUQ9astFa6rLhmsRaXjNGtVJfMpHmqzK+SrKbgUXCv3DcrxSxlRBxTWyxbSNrmODCUidHe6tpKBu9rXXqkjLssRJIjG290ZLyWlyu4wgdfsXcv7ESg0o841hMOl3C4qDfZWZzHPQ1EilbgpGnB9E0CyKhNEsr3KgDDvYnf5/JhamzFf8bSsxakViC7z2+j/f/YQc+h5U7LvNzzUvvwn7gYfrP+i/6LviOBGdifBSF0InvI/CGW7H0Habs9xdj3/tXAGp8Dv73jev40DmNPLmvi2t/uY1nDnSNckAh5rdo3SnEfbnTV4+H4RqZ9CNlbZ2fs5aVj2vh15kUn2AiEiOZUjw9cUFDqYuTFpZQXzKxxvNsNLSYtYw6mJfK3HY2r6rGu/piIgvPHlr3bmnFaD1TYw0vhrcrcdkyboym5pnFyldm7BEfY9BaODibJoolY8mHDKleP2X84c9Mf7ykB03ktLdjgM/99TV2tQ9w9dpablqwlbKH/wusTnqvuF3WOBOTEllyAd1vegD/A++l5G/vIbjuXQyccROqxcHbNi7klMVlfPa+17jhzpd504Y63n/2kgnf0RNiLkt460h461D6RiYRGSmZZS7VkFCtxMpWkBhlrR6Lqkzr52syI/DCyy6mqy0IPdFx32yPVa/HsPsx3FUZj1d6R8lgN8ekAmvrGALshKsSNTRy8exo7YnzfqqCYTEDj0F7GYOVq5iuFaxSaws6cyQ1mYxUsGNlZNKWZZUeDnVPQw92MqBJT44RXn7pqMlJiim8/NK8z8UqVmOoNhKj3vRKzkEsYheatHhEhmg8wa3PHeb/nj2Ex27lexcvYPOBm3E88QCR+tPpu+C7kgxETImEv4Geq+/C8/RXcL/4M+xHnqLvgv8hVr0OrdrLr/71BH7wj/38/oVmHt/byacuXM5ZS2cug5IQs9GZS/P3hHkdVmr8DsrdtqEhffGq42emYNPF4qCxykYwHhhK3DH2fe3EK8Y6k2t0kYazJnTnPZ/U2mYT7/ky91vgd7K62jdi/l4u0frTzCQlTS9nPJ4Yy1ypuc7qor9hEy1HY5Q7Mutq7QI/ezsGhrJvTkat34miKFSPY0HwdBMJCpZXeYbXZpzurh41dx1NJJZJuMe+1uGYFTp/i414Zf5B5nFfA+pAKwmHH/rMeXlLyt0c6ArmzMQ5nWSIoxjyUnOAt932PD/550HOW1bBfed1cukzb8J+8DH6z/gMvVf8ToIzMbUsdgbO+gK9b/glSriH0j9ehvuZr0M8jNNm4cbzl/OzazfgsVv4yN2vcNO9O+kcKLxWjxBz0WhzomqT2ebyZZNMqfE5xpQZci5xWFVObCgt+nkZ7sqCQ0XHa2WVl+NqvBNuyKdYVGUoUcyoVOtQT0xZKo3/HBgamUpykzdz51iPY/fnDLK9Divr60umbJhojc8x6URXE9097l+SPEBxe7lKXGMIdkdbj22GJfwLCa+8ksrSUmwWhUVlLhaUZK6DN1OkB03Q0R/mB08e4L5XWqn22rnl9X42HbwZx6OPEqtYReDy3xS84yDEZEWWXED3Wx7B+9QX8Wz7Ho79D9B/9heJNpzJujo/t73tRH753GF+8ewhnjnYzfvOXMJV6xbM2jkz4tjTWOGe1B3WMxrLicRzp54HWF3jZWWVR97zs8hk1220qAqLy4uzrMi6ej9xXwNRT+2U9gpOl3hJI4bVRcK7YEqOV8yha6NZWuFmW7AXe0k9Mff4bwjEK1cTrzguZ5ZFM+rLOvnkfC0j12LxE6gop03l1MVlM97jFF52CVOVdMRps3D+SnNodGAw97Ij000CtGNYKBrnjuebuPXZw0QTCa4/sYIPOO6j9KlbMFQr/Wd+ntDadyazSwkxvQxnKX0XfJvwsjfgfeIzlP75zYSXXUL/GZ/F5l/I9acv5sKVVdz8yG6+9sge7tpxlI9uWsbJszwttDg2rKzOv1D1WNgsasEeIkVRsFlGaXzMgZ6Q+WSiAVpkyQXmOlGTNsm/t9VprlM3FyjKlARnJS4bXqcVbZKf1+lU6XWweVU1BtXEJ3IARRlf71n5MiJhe9qSIcPipUtRg+0kxjk/caaDMwAKrQk3B0mAdgyKxhPcvaOFXzx7iM6BCBcu9fGFBc9Q9+otqKEOBldcwcCZnyXhqS12UcUxKLLkAroazsC9/ae4t32f8gOPEFr3LoInvJclFRX86I3reGx3B995fB/v+8MOzl9RyYfObZxXWdmEEPNXUTLbZZYg+d9jL6C3qgpnNk7dMNWJOL2xbHb1hCtKzuAMzIXawyuvHNNh7MkbTENz4QpYXuWZdA/0TJvpv9jcqh0xKbGEwf2vtvKzpw/SHAhzar2T29borNz7EyzNR4nUn8nAaT8nVntSsYsqjnVWF8GTP8TgcdfgefpmXC/8GNdLtxJa+w6CG97L+SurOKOxnN9ua+L/nj3EP/Z1cvW6BbzzlIXzLkObEOOVnRJbzFPSYzon+Z3zc1SSRVVGZJfMZ1llMohLDqGcqqGr84kEaMeASCzBfa+28usthzncM8hpVXH+b+0/WXHk96gvdhKtOZG+C75DtOHMYhdViAwJbx19r/sewZM/hHvrd3Ft/wmul25lULsGy9p38K7TVnHp8TX89OmD/HF7M396qYU3bajj7RsXUuqenxdBMTMS7qo5mXZ8rHe7Z5JW7eHlo30jFvwW43Pm0nKJycSkeOwWemKJ2fM+UhTCjZvBKjdWs0mANo8FI3Hu2nGU3247Qnt/mKsrjvCrpU+xpPUBlN1hwksuJLThP4jWnSZ34sSsFi9bTt/rvk/w5A/jeuGHOF/7A65XbiNSdyqONW/npvNfz9s3LuRnzxzktq1HuPPFo7zxhDrefELdmFJPC5FNblhNnVq/cygL5Xw0WgbO8cp3OR45JEyu22J8TmwooTcULXpG1Ay5kpPMIsVKKCMB2jzUE4ryu+eb+P32ZhyDHby/4gWucT6Kr38viYiHQe0aQuuvJ16+othFFWJc4mXL6D//Wwyc8Rmcr96B6+Vf4X/wAyTsPrzLLuEra6/mHRtP4KdPH+HXWw7zm61H2HxcFW89qWHSSRyEECLb+no/pa6p7a2/YGXV6BtNBbkxe8yxWVSZBjBOXocVr9PKqpqZbUNIgDaPtAQGuf35Jh55cTfnGc9wu3crq9mBMpAgWnMCfRu/weDyy8E++gROIWYzw1lG6MT3EdrwHmxNT+PcdReOPffi2vk7NrqqWNd4Ic2v38QvWpZw16sd3PdqGxsXlfIv6xdw9tKKoQVihRBiMqajZ3BWJZAQ4hhnKVJiGQnQ5jjDMNjRHODeLa9gO/AoF6vP8t/WHViIE3M2Elz7IcIrrpDeMjE/qRaiC88iuvAsOOfLOA48hH3f33Dsvodlr97Ol6wuPr30HP6hbuR/Dy3kU/f0UOK0ctGqai5bUzurUy0LIURB0gMmxLwlAdocFY4l2LZ9C50v3sPa4D/5trIb1WYQcS8gvPJ6wiuvJFa5Rr7AxbHD5iK84nLCKy6HeBhb09M49j+Ia/+DXDzwABcDfRVL2KKs5c6XlvP+F1ZRU1XDBSsrOX9FFY0VxVkwVgghhBAinQRoc4gy2EP7q4/SvfMR6nue5QpaAGj3aQS0/8RYvlmCMiEALA6iizYRXbQJzvkyls6d2I88if3Ik5zX9AjnW+/BsCrsCS7j4WdX8eOnNTpK1nOy1sg5yypYVeNFlc+REGJWk+8oIeYrCdBmMWWwG1vrC4T2PQUH/0H1wE4qMeg3nOxzr+elpf9O7YmXg7+eaLELK8RspSjEK1cTqlxNaMN7IB7B2rod+5EnWXzkKd7bcj/vM+6BEOx+vp6tW1fyiHUVRt1Gli5fw2mN5ZIJUggxbxhWc7SAYZNRA0LMVhKgzRaxQazde7C2bkc9ug2jaQvegQMAuA0LLxjLecj1Fjwrz2f9yeeywC1frEJMiMVOrO4UYnWnwCkfhWgIW9t2bEe3sqDpWa5u2cq1scegGTqa/Ox4bClbHCsIV6zBu2gDG9ZvoMKuSg+bEGJWSLjHtjjw0Pb+BiJWB4Z7hrJFCiHGTQK0GaaEe7EEDqP2HsDapWO070Tt0HH2H0QlAUCX4WNbYgXbjVPpLV/PgpWncYa2kItKZ/daEULMSTYX0frTidafDiffQMhIYOnajbVlK/H9T7OubQebQneitv4BWqHnOQ+vsZQW9woGy47DWbOSyoXHs6i2GpfNUuyzEUIcKxSF8JLXgXX8mSRnIjgzXOUYdh+xytXT/lpCzDcSoE0BwzCIRiKEA23E+lqJ97dh9LeiBNuxhDqwB4/iDjbhG2zGlegf2i9hKBwwathlLEQ3NnBIXcRA+RqqG1Zy4qIyrqnz51iYUggxrRSVeIVGvEJDPf46DKAzFsLSsZO+gy8Qbd1BXfvLnBT6C7bQXdAMvACtRikHlHo6HAsJe+rBtxBZSOSpAAAgAElEQVR7xSK8lYvxVdRT4XNJACeEmFqzedkc1UpkyQXFLoUQc9K8bv2/2NRLU+8gCcMgYUAiYZAg+X8DEoYB8ShqfBBLLIglPogSC0I0BNEgSiyEjTBqqAd7rA9HvA9nvB9XvA93YgCP0Y/H6MdnDFCt9OcsQ5/hosUo52WjiibOoMtWQ5+zngF3PUbZCmrKS2kocXJOlYe6EqcMmxJiNrK6iNeeiLv2REpL3fT0BOmJR1F69tPb9Br9R19D7d5D5cABjgs/hS/cB13AQXP3qGGhjVJaKaHfUkrIVkbYXk7MUQYOH6rDh8Xpw+byY3f7sTj9WJw+rHYXNocTu92B3WrFqipYLSpWWSdJCCGEmLemPUCztmzD9+jHIBEDRTV/UM1Mg4qKoaigWIZ+T/0YaduY++RgGLkeREnEIB6hrrWHhUYMK3HsxLAqcWzEkj/JfyvxMZ9LGDtB1UNQ9RGyeglbK+i0NtJi8xFxlBN1VhJ3VWG4q1C8VaieapxuLy6bhUaXjTV2C4oEYELMDxYbRsVK/BUr8a+7fOjhQSAc6SfWe4RAy35CXQeh9wiWYBuWwS5qol14oocpCffi6IuM+eWihoUoVqKKFa/LDRY7hsUGqpWR36mp783h71Aj+3sWJS3jq0Lo+OuILLtkCitICCGEEBOhGDmDHCGEEEIIIYQQMy1P15QQQgghhBBCiJkmAZoQQgghhBBCzBISoAkhhBBCCCHELDGvszhOBU3THMA24MO6rj9c7PLMB5qmLQO+A5wFDAC/A27SdX2wqAWbBzRNOw74AXAa0An8QNf1bxS3VPOLpmk/A5brur6p2GURUyP5Pf994I1AGPi2rutfL26piivXtU/TtHLgFmAzZp7Sz+u6/su0fdYDPwbWAzuB9+q6viXt+TcBXwHqgIeAd+u63jYzZzQzCl3fNE1bDPwUOBM4BHxU1/X70/Y9D/gusBx4Drhe1/U9ac/fAHwSKAH+CHxQ1/WBGTmxGVLoGib1N3bZ16nJfDY1TVOALwHvBmzAz4FP6ro+9ix7c4CmaW8FfpP18J91Xb+yGO896UErQNM0J3A7cHyxyzJfaJpmB+7BbASdAVwHXAl8uZjlmg80TbMB92N+eWwAPgB8VtO064pasHlE07QLgH8vdjnElPsG5vfRhcB/AJ/RNO0txS1S8RS49t0KVGA2Ur4I3KJp2hnJfTyY3z/PACcB/wDu0zTNl3x+I/BLzIbeaYAf+NV0n8tMKnR9SzZy/4wZdKTq4k5N0xqT+y4E/gLcBpwMtAB/1jRNTT5/NWbdvR84L3mMb83Yyc2AQtcwqb+xy75OTcFn8yPAOzFvYF0FXAt8fLrPowhWA3cDC9J+3lms954EaHlomrYa8828rNhlmWdOwbzD8E5d13fquv448FnMC5mYnHrMOzcf0HV9j67r9wIPA+cWt1jzQ/Ii9xPgqWKXRUyd5N/13Zg9Rdt0Xf8z8HXgg8UtWXHku/Yle4YuA96j6/pLuq7/ArNB8v7kJm8GosDHdF3fidmo600+DnADcKeu67fqur4DeDuwWdO05dN9TjOo0PXtPEDDrL9XdV2/Gfgnww3pdwMv6rr+dV3XXwXeBSwEzk8+/2Hg+7qu/0XX9a3AezEbj96ZOrkZUOgaJvU3BnmuU5P9bH4Ys7f8cV3X/47ZE/SBaT+Zmbca2KHrekvaTw9Feu9JgJbf2cCDwOnFLsg8owOX6LqevrK3ATiKVJ55Q9f1A7quv1nX9ZCmaYqmaWcC5wCPFLts88SXgb8nf8T8sR7z++fJtMeeBDZqmnYsTgPId+07FTiaPmwHs55S250GPKXregJA13UDs5GY/vwTqR11XT+MuZT7fLrGFrq+nQa8oOt6X9pz2fWXXj9B4HngdE3TLJh33Z9I2/cZzGkqJ0z1SRTLKNcwqb+xyXWdmvBnU9O0OsxgI73ungQakj1H88lqzM9wtqK8947Fi8+Y6Lp+S+rfmqYVsyjziq7r7Zh3xABIdgF/ELPLXUydI5hjye/FHO8sJkHTtNMxh3esAT5W5OKIqbUA6MqaA9sK2IEq4GhRSlUkBa59C4DmrM1bgYa057MbN62YQ9XGsv+cN8r1bSz1l+/5UsCZ/ryu6zFN0zqZR/WXJfsa9h2k/goqcJ2azGdzQfL35qznSD5/eHKlnh2Sw5OXAZdqmvZFzA6sPwCfp0ifXelBE8X2bcy7CJ8udkHmmSuSPycC/1PkssxpyWQJP8ccAtdd7PKIKefGnDOULvW79OwPy1dP9uQcjXzPp+pwtOfno/Tr22Tqx532e77955vsa5jUXwGjXKemuu7m4/fjCsxOqwHgGsw5dtdhfoaL8t6THjRRFMkL+ncw5y9co+v6K0Uu0rySHOeMpmlu4Jeapt2o63qkyMWaqz4H7NZ1/Q/FLoiYFoOMvFCmfg/OcFlms3z1FNJ13dA0Ld/zqToc7fl5I9f1LVk/JVmbjqV+OpPPkef5eVd/MPIaBvwCqb9CCl2nJvPZTK+7aNq/Yf7UHcnPaKWu653Jh15Mfo5vx8zeOOPvPelBEzMuOezjF8D7gDcnJ+WLSdI0rV7TtMuzHn4Vc6iWvwhFmi/eijlhul/TtH7gE8DZyX+Lua8JKEsOcUmpxbzD2VWcIs1KTZj1kq6W4SGgk31+XihwfZtM/aQaekPPJ+dHVjCP6m+Ua9hRpP4KKXSdmsx7ryntd7L+PV/qDoC04CxlJ+ayAs0U4b0nAZoohm9hfplcrev6XcUuzDyyCrhL07TqtMdOAtp1Xe8oUpnmg02YY/o3JH9+CmxlePy+mNu2AxHMtOgpZwHbdF2PFadIs9IzQL2maUvSHjsr+Xjq+TOSd51TvUhnZD1/VmrHZIKBRWnPzxf5rm/PABuSWfZSsusvvX7cmMMjn0kmd9iS/jxmgoIY8MKUn0Hx5L2GYSZlkPrLbxP5r1MT/mzqut6MuexBet2dBTQnk4nMC5qmXa1pWmvWjboTgB6K9NmVIY5iRmmadhpmytFPA1s1TRu6q6DrekvRCjY/PI55t/FWTdM+hjnh9WZkjblJ0XX9YPrvmqZ1Yw7r2pNnFzGH6Loe1DTtl8APNU17J+adzhuB9xS1YLOMruv7NE17APiVpmkfxGw4p9LHg5nI4Wbg+5qm/RAz9bQfuCP5/I+AxzVNewqzQfNd4H5d13fP4GlMq0LXN8zv54OY389fAC7FzP6WStX9C+DjmqbdhLkW02cxG8apLLw/BH6qadqO5OM/BH6RlTFyrit0DZP6K6DQdUrTtDYm99n8EfBVTdMOAXHgq8lt5pPHAQX4iaZpX8Gck/aN5M/fKcJ7T3rQxEy7Jvn/r2J27w79HKMpraeMrutRzC+OGPAscAvmPIjvFbNcQswBH8W8y/ko8GPgi7qu/664RZqV3o55R/lZzDkv1+u6/jSArusB4A2Yd+afx1zM+pJUaurkdu8GPgM8jbkO0ztm+gSmWd7rG2bj7wqgGtiGWZdX6bp+AMwU88DVwNswez5qgCvSUqPfgbnY7Y8wM0VuZZ5llC10DdN1PY7U34RMwWfzG8BvgTuTP7cD35yp8s+E5PDGzcBizDr6Cea14KvFeu8phmFM2QkKIYQQQgghhJg46UETQgghhBBCiFlCAjQhhBBCCCGEmCUkQBNCCCGEEEKIWUICNCGEEEIIIYSYJSRAE0IIIYQQQohZQgI0IYQQQgghhJglJEATQgghhBBCiFlCAjQhhBBCCCGEmCUkQBNCCCGEEEKIWUICNCGEEEIIIYSYJSRAE0IIIYQQQohZQgI0cczTNM3QNO3GYpdDCCGEmG5yzRNi9rMWuwBCzAKnAweLXQghhBBiBsg1T4hZTjEMo9hlEEIIIYQQQgiB9KCJY4SmaacC3wBOAKLAI8CNuq4f1DTNAD6u6/o3k9ueB3wNWAvsAz4K3Adcr+v6rZqmfQG4FPge8DmgHngKeBtwOXATUALcA7xH1/Vg8rga8CVgU/L5ZuDnwJd0XZc7JUIIIaaEXPOEmNtkDpqY9zRNcwN/BZqAK4B3AycCd+TYdi1wP9AKXA3cCvwesGRvCnwS+DhwPXAa8DjwLuD9wFeBtwL/mTyuF/g7UAG8A3gD8CjwRcwLnxBCCDFpcs0TYu6THjRxLDgeKAe+p+v60wCapnUA52ualn2T4lPAEeAqXddjwP2apiWAb2Zt5wXepev6s8njXQq8BVii6/pB4F5N094AnJrcXgP2AG/Wdb09uc8jwFXAuZh3HoUQQojJkmueEHOcBGjiWPAa0AXco2naHZhDNx7Vdf1xAHMUxpBNwB+SF6qUPzDyYmUAW9N+bwXakxeqlE6gFEDX9W3A2Zqm2TRNWw2sxBx6YgMckzo7IYQQYphc84SY42SIo5j3dF3vA87BHIP/DsyhHy2apr0vx+aVQHvWY605tgvquh7PfqxQOTRN+3/JY78CfAdYgTk3QBntHIQQQoixkGueEHOf9KCJY4Ku668Ab9Y0zQ6cjTlO/oeapj2ftWkzUJX1WPbv46Zp2tsxJ0u/H7hd1/Xe5ONtkz22EEIIkU6ueULMbRKgiXlP07SLgF8BxyfHwj+iadoe4DJgUdbmTwBv0DTto7quJ5KPXTEFxTgdOKLr+o/TynUi5oVQ7iYKIYSYEnLNE2LukwBNHAuew7wg3KVp2teACPBhoAd4LGvbm4HtwJ2apt2COW7+v5PPJZi4LcB7NU37HGbmq1XA5zHH9bsncVwhhBAinVzzhJjjZA6amPd0Xe8CLgIGgV8DdwNO4EJd1zuytt2JeZexEfgz8B7gI8mn+ydRjFuBrwPvxZwPcAPmGjU/x0xXLIQQQkyaXPOEmPsUw5C1AoVI0TTtQqAvlUo4+djrgQeA9bqu7yha4YQQQogpJNc8IWYnGeIoRKbTgI9rmnYjoAOLMRfWfEIuVEIIIeYZueYJMQtJgCZEppsx12j5FFCPuZbM3cCni1koIYQQYhrINU+IWUiGOAohhBBCCCHELCFJQoQQQgghhBBilpAATQghhBBCCCFmiYJz0Nrb+2bN+Eev10F/f7jYxZh1pF5yk3rJTeolN6mX/MZbN1VVvjm7CG0oFDHkfTAx8hmaHKm/yZH6mzipu8mZrmvenOlBs1otxS7CrCT1kpvUS25SL7lJveR3LNXNsXSuU03qbnKk/iZH6m/ipO5mpzkToAkhhBBCCCHEfCdp9sXsFI+itL6E0fYK9OxDDfdij4ewxgdQVQsJdyVx/2LiFccRrT0Jw1lW7BILIYQQQogi6hiIsKOpl3OWV2JV5+yIewnQRPH0DcbY3xXkQGeQ/V1B6NzFip5/oA2+yNrETjyKOSY6bFjpxUu74SSIEwsJqtQtVNIDgIFCsGItCe0KIiuvJOGpKeZpCSGEEEKIItjd3k80bjAQjlHishW7OBMmAZqYMU29IbYd7uXFpl62NwU41B1kpXKESyzP8g7Ls6xQmgBoti3hJd8lHC09kR7/8US99VhUC6FonGA0TjASp30gQndXJ9auVzkh9hIXtm9jbed/E//nV+hdcglc+AlwNBb5jIUQQgghhBgfCdDEtDrSE+KRXR08rLfzWls/ACucvdzge4bzS/9OxeBBDBSidafSt/x9RJZehM1TyzJg2RiObxinc7A7xBMHe/jZa9tZ33YXb9z/MLaf3YNRvxnL+f9Fwt8wrecohBBCCCHEVJEATUy5eMLgib2d3PF8E88f6QVgQ62T/12znzMH/kZJy1MofQkidafRt+I/CDdehOGpntBrKYrCknI3S8rdcEIdbX0X8KPndcpe/jnXHbkXy23n0r7uBhynvx8s9qk8TSGEEEIUoIQ6zTniytTmpFMGezCcpVN6TCFmEwnQxJQxDIOHd3Xw038eZH9XkAV+BzedbOWK+N+o3HcX6p4e4t46gifdwOBxbyJRsnjKy1Dtc/Bv567DdtGP+OUD17F8x1d53YvfpF3/I8pF38aoP2XKX1MIIQSoAy3YmrcQXnYxqNK8ONYpgz3YD/+DeNkyYlVrp+y4al8TtqNbiC7YSMJXP2XHFfOPpeNVrF27CK+8sthFGTf5BhVTQm/t5xuP7uHF5gCN5S5+flo3Z3bfjePlR0C1EG68iMHV1xJtOAvU6V9zw+Owcs05p9Bx4h18//7bubLlu9T96Rra1n4A61kfk8aDEEJMMWvHq2DEUSL90rshIB4BQAkHpvSwSqQv+f8AIAGayM/atavYRZgwaaWKSYnEEvzsmYP86rnDlDvg12tf4vT2P2DdvoeEq5LgyR9icM3bSHhqi1K+Sq+Dt7zxnTz+6rnYHr2JK176Pm0Hn8ByxY9J+BcWpUxCCJEhEcOx515ilauJl68sdmmEmD7xKEosiOEomcRBjCkrjphZan8zJGIk/IuKXZRZTxaqFhN2sCvIO3/7Ar969gD/1fAC//R9krN3fxXD5iZw4XfofMezBE/9eNGCs3Tnrm5k6dt/wbe9N+Ls3YXrNxei7nuo2MUSQghIxACwdO8pvF1kAPv+hyAWyv18PIq1ZdtQz4UQs42t6Z/YDz5W7GLMmMPdIR7b3TF1B0zEZ8fnOzZoftck4uPazdb8HLaW58e1z9HAIB394XHtMx9IgCYm5NHdHbz9thdY3vcs2ys+y7+2fwOcZfS+4Zf0vPE+wto1YHEUu5gZav1Orn3bf3LrqlvZH6uk7P53oT7zP2DI3TghRBGlEigYiYKbWXv3o0QHsPQ15Xze0rMPS+Dw6IGeEEWiDnZPfN/eAxDNc3Nilnq1pY9IrPDnejzshx/HsfevU3a8ibJ2vIolcBi178i0v9aOpgDbDvdO++uktAQG6Q1FZ+z18pEhjmJcDMPgtq1H+N0Tz/Nj3x2cE/0HMdtSei/+GZHGzaDM7lXbrarCtRecyQPVv2Hv3z/J5du+RXfby8Qu+h7YPcUunhBCTLvBaBy7VUUt8H09GI1jURVslgncx53l1wEAa/tLqL0HiSy/dMz7hGMJDMPAaZv+edTzXmRgfNfceBRb63YMu5f4MZwYZKrn881Hk73n/mKTWcebV00su/hUkR40MWaxhMHXHt5N61O/4DHXxzk78RwDp9xI91seIrL0ojlxUU7ZvHYJritv4Zu8Hf/hB3H+/krUgZZiF0sIMQ6apjk0TXtZ07QLC2xzgaZpWzRN69c0Tdc07d9nsozTYhItEMMweHxPJzuaCjf0Ht/TyRN7Oyf8OrOdpXsvSnJo6Vj9fXcHj+/pPCaHW02ZZDvBceAhlHH1piXf83Gp+0I6BiL0h8f3vp4rjGNstJMEaGJMBqNxPnv3Fs7Z+Rm+ZvsplroT6HrLwwQ3fnjWDWUcqw0LS7ngus/yCdtNqD37cd/xBiwdrxa7WEKIMdA0zQncDhxfYJsVwL3A3cAG4IvA/2qadtl4Xy+emM7Gwcw3PFr7Rm/oxuLFaxBZW7fj2PUn4gmDWHzqhohNhZkcbjX/DN/IVSL9RSzH/LTtUA9P7euawVecue+IB19rn5LjhKJxHtjZRs8sGMZYiARoYlSD0Tjf/cM93NT8fi6zPMPAqZ8kcMXtJEobi120SWsodXH9W/+Nj7hvpjcUxffHK7EdQxOYhZiLNE1bDTwDLBtl0zcD23Vd/4qu63t0Xf8N8CvgukI7tQQGSaTdrQ3HEjyst3OgMzhq2R7f08kLR6anAd/WH2FXez/5G0WZj7cEBvnnvk4sPftGnd82JabwDrel9wAATx/o4pFdU5hkYayMBPZ9f0PtPzrtL9XeH+Zo7+DUHjQemaXzq9MCtMTsbCA39w7SFZzaRBxzrffHMAwGIhPridvTMcAju6YmmJoOnQPm37apZ3bPZ5QATRQ0GI1z++9+ype6PkK1M07gqt8TPPmG4Unt80Cl18Enrr2CT5Z+Gz1ajf/ed+J8+bZiF0sIkd/ZwIPA6aNs93vgg1mPGYAz3w6tfWFebAqwr2M4GBuMmpnKmgOjN6IHo3HaxtA7leLY9Ses7a+Mads97QMMRvIHWtbO1wCIRGO82NTLi00BjO4DWNpexDJH1wMaCI8vSxyxEMQmH+wo0SBKbBBr+8sT2l8NHDYD41zimYHJ84d72XpwCns9EnEce/+KtX3H1B1zGljbZmf5XmoOsOVgz/h2ioUgOvoNnLlif1eQJ/d20Tc4/iBtb/tAUXvfRzNXYuX508oWU24wGueh336FG3u+TKDkOELXPUS07rRiF2talLhsfPFN5/G16m/xWHwdvsc/hefpr8ydT7IQxxBd12/Rdf0Tuq4XbBHpur5L1/Vtqd81TasB3gI8kW+faHI43WBsODCY7um1lsChKT3ewa4BWgJmkKgaZgNrvPOtssXiCY6OIUA1FW8+smPfAzj2/W1Sx+gYiPD0gS6MSQzfsrVsyxmAqL0HcOy9b2ix5WmR/JurfUfMOVvjSIX+9IEuth0eZ3AyRV5r7WNn6zjqZZzX51eOBiY2rzIeQe3ZX3ATx74HcOx/cPzHnqVSw/9C0XHeIJmllIE2dh9uoqXQd5hhDC15MhtIFkeRUyQa48XbPsK7gndzqGoTrqt/imFzFbtY08ptt3Dzv2zks/d+meaD3+Ztz/8QJdhB/3lfB1U+KkLMZZqmeYC7gGbgx7m2sVgUfH4X7kAYn89JaakbACUUxe0ZwOuyDT2Wj9tjBxh1uxTFkzaHV7XiLrCf02XDHrfi8zkhx3apY7nCdtxWsxzOhA2P24GR3McwjDGVMXubrQe7Odo7SEO1D5/TNmJ7i0XF43VCOIKrxA3OsZ0/AO07oawRrMMdm6lzcccnVp+uUjcYBkrzNoxKDRy+/Ntleb61n4TVikWx4fQ4hv4mqTrJVR7DMDAMUFWl4PGVQA94HLicMfAPH1dV1cxjxiMoux/AWHQmuMtpCQxiGLCgJG/nb9q+VvP1LTY8LY+Cw4+x/HWj7wfE1B5ixtjruyCrC6XHAR4nRuqz5LFnDLdNr5/2Q2ZgmPO1084JpwEeB4QPYpSeDJjvv9HK3HWoB8VmGfdnWDm0HQaOYtTUg6s05z75/t7px1Km4E5PofdtrrKPRa66K2lvp/TIY/hXXJHzWMqAC+IODL9rxHdRoTKMVv5cCn3ucvF6B4hbVEpKXLi7kt8jPduwHulmt3EJ6+pLcPeF8flcGcdTDj8DgSbcHnNghsvjAEXBYS1OX5a0OsUI8ViMQ7+5nkuDD/NK3ZupvuLroB4baYXtVpUvX76WLz3wKTr0Ej7y2u9RQ10ENv8I5nmAKsR8pWlaCWaykKXAWfl63uJxg75AiOBAhH6bSk+PuVlgMEpwIIIlnhh6LJ9gcn7DaNulOAaGh0NuP9qN1drGiipvzm1DoRiOwRiBQJCEzTy+7chTYMSJLjwH+8AgR3vDdNp8BB1mOayhCAO2MIm+QWLOIIZhjKmM2du0dw8QDMXo7A4Sd40M0EpL3QT7B1EiYSK9QYxBK7F4gr/v6WRDvZ9Kb+5kUspgN/ZDL5BoPUK04cwR9RKMj70+nz7QRe3hTo6r8RLuCaKEurA37SLa3kZ3zZmUJMttGAbRrgF8DivhrOMqkX4S7fsJGT6ORgaoMmxEk9uk6iRXeZ472E13MDqUmjtV/nBPkFda+lAVWFXjw9Y/iDoQJhoYJJEYPq7bY884ptrfjC3QR+LAi0TrTuXxnW1A7tTfLx8N0NQzOPxcPIJjIIxhSaDEozDQPuI8synhXuwHHyMeWkXYXjbm92+GaBDH/geJLDwbNdgBnTpKIo5iDA7VoWMgAsZwr0x6udLfc4e7Q7T0DbJxUdmIczISfbS1BChxWlGT+5eWuqfss5m9na2nF3UwTKSnHyNsz7lP+t8b4IGdbdT6HUPH6u4JZi5rYRhmPYzz5m/264xW9rHIVXdG615ioTCDR/fSYx15U8DaP4hlIEw0ECKhZO5bqAyjlT+XQp+7XNq7g8TiBr29oYzv18hgjOBAhEBgkOBAhD67hd7WIIbVBRY7jqPmkOTUd86fth4GRk+3X1XlK/j8RMkQR5HBiMdouv3dnD7wME/Vv4fqK795zARnKVZV4XMXabSsu4Gbou/CdvARSv7yVpTB4gz7EEJMnKZplcBjmMHZJl3X9xa5SAWl5r6p/c3Ymp/NuU36wC412I4aMucvhWMJOvojBAvMU5tJ/eE48YTBno6B/BulelOM3EOp1HEkkgiEYkRiCY70DmYMx3ytrZ9nDgyndD/QFWJ/R5DA4Mhj2w8+SkX3iwC0BsK0948tWUR3MH85j3SHONQ1fQkJmnrShm0ZBkosNPTvsVKDZlIHT3jiy82oQTOItPQexNq5k1ebenjl6MSGcr7a0kfXQHqdDp+LYRi0BcLsbi/wvsrDEenGsetPYxpi2hOM0tw7mDbGeXxDKlPDjHOxdryCY8+94xp+OiUS8XG95mSG+Y7G2rINS+fOCe3bMRDh0V3txHJk102f/xaNJxiI5D9f+8HHsB95ckJlmG4SoIlhiRhtv3s3JwUe4qHqd7Pyys/NqbXNppKqKHzsvGW4Nr6LD0Q+hNKynZK7rp6RjF5CiKmhaZods+esEjhH13V9MsebiqZKbyg6pnkdtubnRv2+ORoYHHMAkTKWc1ADR/CEmjIeK9aVwBc6POIxJRzIOVekqmc7AF39kYx13gazGmjBZHa6aK5EBlnZLnM1ACdlOuc1GwksnTuxT1cm4nhkRIKTTDP8LplAVXoGzc+UpWvXKOcCzx7s5qXmAOM5LyUcIFggIEhRAwfNfxiTn/MUiSUy3qe1Xc9iyZN4yLH3PgZe/gsP7Gwr+N4e8xlP4v1sCRzG2jmxr+Td7f1E4wYDBdZ8UxTY2dLP3qxAPrvEE17820hM67p8EqAJk5Gg9873sqb7If5S/u+sv+ZzxS5R0SmKwn+cuQTt7Gt5e+QTRHsOU3LnlVi69xS7aEKIPDRNq9I0LTVG8CPAScC/AQOaptUmf8rz7T/deYGeOdDNE7J6q0wAACAASURBVHsmuAB01o38HU2BMaVnH+852Vq2UtX70vh2KkBJFnwgEuOBnW10TyaFuZHAfvBRbEefG/GUZ3C490dJRLFOcF3LSCyBkbo5OV1viHHc/CyY2CDJHu3BsfsvWKcxW6dj719x7L1v0sfpCUULriuYnpL+QJc5RHQ8lFDXqD1klsDhvD3UI44X7k0VbNRtjb0P80LTeJbZKPA+MAyzh2mUIOCx3R0ZyU+ckW6s3btHbGdtfxmMBG29ZsAymOdGkbVtB57+VAbScQbdhjFln5lC6x/m6tPsCkbynlNOEy1n8iaO7ehzOPbeP7FjjIEEaAIMg/6/forlbX/jdu872fjmL0zJZNb54rqTG7jgwit5U/iz9A8MUHLnVVhbXyh2sYQQuW0Bbkz++42Yc60fBo6m/fxltINUHH1saPiNvfNVarq3ZDyfMAz2dgwMrZe2q62fQ93FWVfnSG/mum25GFPVuzGWNk2e60dncsja0QJDv0Z/fbMAarDw2mhl/btRQ+NcPy1XHU5RtXlCzdj/P3tnHh/JWd75bx19X5Ja50ijuafnsGdsbDO+7bGNbYyNTcDcIRwJBLK72YRsNhDIEpJwJJBlMRtYCAkQMDc24TSxiW1843PGV8+pOSSNbqnvq6r2j+puVXdXt1oaaaSZeb+fz3xGXV3HW28d/T7v8zy/Z+A+6nWgOzOGnByx/e65wbln+N25qTnXKaHpBvftG6sqVr54v/ly0t7z+9SxaZ4bmuHoZJrjM/WflejobAHr6EiC41XP1Vy3oPPYg8W+tievGxgYyJm5Sxv40kPzUkA9OpVCHX8RR8E0ENVCCl96qHxrybHjKFMHmzIO5NQo6kS0Rg3UwCBbqDRe8oW5w5qbnVyuWx6iAvv7xXnoF/SPFfu+kK70SDdTi9EwcBx/mMnR49y3b7zuZE5p0sd6M/z2yDQPH26+XIUjP/96lUZqHNf+f0dKjiInFh4O3AxCJERA9qH/zbqBO/me+loufvPHUGVhnFVz67k9+Jyv5vU/9/BvfJruu99E7NVfJt9/9XI3TSA4q4lGo1LV57WWvy9c6H6VQgp1IooW3opz+gCebBJroMzRyTQHxpJIEqwP+zjcRBHrBWEYZYOnPGtcNbibTOTQ4lm6bDZfSW/zvO2M+NwtDM/sRUrtwPB2NH0siQXk4Z1kMW9/6hjKdAKtZX3Nd+2xvUj+AIaN4AJA+8RvceAlu/m2k2pDM0ymchQ0g/1jCboC9uItC6J0n9YJHRxP5PAa5hW3DS8t0ih3azF4aThOm99JXxOKgM6C1RNX2ebxZI7O9AFwhcrLdKAlcYiWxCEGum+kZ/JRFD0P7ARM7zSA0Yw4SPF+lKryM4djWZ7cP87Vm9qXTmGwyReHZOmTRLbA6NAEsm4gGRquQ/egBfspdL+CsUQWl2zQ6Al+6tg0LQ6Dbakx9JFh8F7JTFUdtkxe45GBqbJBWp0jN5/6a1KTz7uUHEVCZ5Q2Dry4nws9Gq75Tv4sAOFBO8vRn/0WfXv+kZ9zBZE3fQa/S9js9bgu0sF/u+1abs9/jINaJ6GfvhNX9EfL3SyBQLBEGBgV+QlqIYk8Y+aOFIpGkqbpqCeewlFIWDasDfPJFvSGITtgDsTWnvglctya/3Xy4UIrpZrjgWIuyFS6eeEPgEB6EOfxh5eiSVWcXE+1x16orX1W11PSrOm88DbV8/w8fWz+noOVgqTlUep46JpB0U3jbyadZ/9YknuK6pjzJZbJ89TRaUYO78Ex/Nu66ynGXPf67H2gTLyM4+j9NblhcuJEhUewVDzafsLDRGvgoZtPgFTdvdjs5MhUmnzB3MKTNQ0YOWl6mZ4+NsOjA42F1sYTOQ7NMdG1dzhW4S08mSc2J9krclbjHHwEx+BjTBTzfZM5DTm3wLy1eSAMtLMY+cA9tD/8IX6j78Bz6x2sWoy6J2c4l65r46/fcAXvMj7Gk0aE4L3/Dc+zX1nuZgkEgkVmMpln72Ac5fDswKhz7GEcVeHNueQUSuwYHTPPlZetHbkHdeTpivXu3z/OQ4cah99IxUGV44RlW8Pg2FTazK1YAaHnBV2vFUHQCxx98BvsHRi036gK/SSEN6TSwGjO2e/6fXV8uiqnq9FgFjMnraESpYWspcB5Oq+R3vMDFG3xQl/veWmUVE5Dz6fIjDYXspbXdJ453qRRVq8vCs0WKV9enjw6zf6x2ckSKRsrqjYm8KeP48uYYaSabpDOWownLT+3uqGlb5zHH6Ylvo9skzlPrv13myIrtTst/xU7tofnDx7lvugYhyYq77f5FjZ/YSjOVIOcLE92rHJBnTw3c67JbGMmr5mqljZk8lqFZ789VsxhtbyzJAx0w7BVT20WveqxX0gaWamdJ2aK5yzNX6l8qcMbQRhoZy3K0G8J/Or97NXXMnzNFzmnr325m3TasLM3xOffehkfcv8Vv9B34X/4r/E98ndLry4gEAiWnOqn2JrbVe2RcOTjzEzaz8ArsUr1QbWQwj+5t/yemEg2EMqwhDRl8gVePBHnacsAe95vmkYbNCm5Xcr5GHj+IQ49flfld4W0eT5Vx1kUiW6j2ptQ6T0ZjmV4fGCu/KvZfdR4J2aO4Nr/Y8hXz9zPDiyfPxHj4FiSQOoI8hyCDdGRJA8enCBb0Nk/muToZBpntXe1iDIRRcrNfndoIoUjelfFMjt+c2Ccoef/kyMvPEIuM3do7UsnEozGsxybMnOCHIUEPROPVBiOcqLWuM5lUmQy5jpWD2b9HKUmJhDsJhkKaVz77saZP/lSNhPJHGlLmQk5ftz8PzGEO2cR56m6NV0Hf4bz2ANNH0fNTNCSPDT3z77leylvb+TLiSHUsecrPMuLEeb5xJFpHq3zbARTA7MfUhPkXvwJL++vlbzfN5rg/qKoUUnV0jRwKq/jAwcmKso9yHW8t4MzGQYm0mUv4JzM0b92Xytamq7JJ2AeJTpWIsJAOwtRpg7i+fd3cFxr48Hz7mD39jXL3aTTjtWtHr741lfylfYP82+F6/A+80X89/3JnLK9AoHgzKF34mHCMXulwD2DMY6/9DDo5sxye2wPgdQxXMVB6JNH6w9GY5k8iaIUvFH0FFnDmY5MJGvy0KxY1QwBU5ChODC2bqaMv4jrwE/mVRcpkB7EUUghJUdx7bsbCmlyVQIF6uieWeU7G1I5rcZDUA/ZIkM+ncpT7XzbMxhjep4hk1aUomEi5+J1J9k03cBRSBCOvUTnzLO263RPPlb+O53TGhjgxWNoedSJl3BYajAlMgVSeQ05MUQmrzNpJ5Bg6KwduYdcwvTGGjaexLymc2w6XWEgq4UU6byGeuJpescfwpWP4UseLe+zVEvPypHHf8DhojFu9eAoUweLx164AV7QjPI9XarBFkyZ7akWwKhmIYfV5ggvBtPbtm+0vnFcyrfSDYPxYgHkhU5C5AsaewZjjMQyOIaesBXwkEb2cCKeqX8Mw8B56B68mfohn9XPZjWZvMbwiWEOT6RITJ6wFRvSskmcA/eSz9g8s3NcDN2oVIssXVvd5n4rcWgiRTy78PpwrYl9eHKTKBYvV0k1tGfiEaQaw63Ja1icODtVcQzCQDvLkNKTOO56G8mCxNfW/D1vvnzHcjfptKXF4+Dzt5/Pb9b/OZ/NvwFP9AcEfv4em5lYgUBwOjCRMAdd4Zh9DaGG2AxUkicOosSONJTdnkzlakJ+BibSHBorFqwuDgoyeb18iJF4lpjNDHTpe1WrLFw8kyoZewb3RmdDm6TpAfMPPW+GOOWbD8VTZw6b7UtP1YTPyakxHIOPNtx+/6i9gSZlpivCrUJJ8zh5TefxI1McGLe8XxuEOZZyYE4WCcPUaSkeS65Tt8qdsxjchm57fYpfVu6/kKn1yhkGzw3FOD6VQasSPSjdD7O3W+1w8fh0hqlknpn0bBskQ0MzDJSiR6n6eHbouukJkdK1ZSEyuTzRB77J6PFaOfdmyBV0XjqRaHj8RaG473QTKofT6XyF2I9aSFbklhqGQXQkwf6xZPmZar4dlR8zxVDYwTohg/FMgfHpaUZjOSYS+fLxp1L5cnc9enAULZeifSHvqyJPHJ3mpRNxMEyl15JBJ1nuK396ECmXIJC23DvFCZ9ScfJ6HJ5M84ClrEhpr97BB+tuk8gUSOftr1f1ZEy2oFVcM196CE/RU+oem51MKYUou/IxlPJkg1H1f2M88cNNrbdYCAPtbELLovz4XThTI3ym5a94301XCzn9k8SlyvzNzVtJXPjHfCj/HpxH7ydw15uQMs1LHgsEgpXBI4cmGJpJE0hVhieWQo/Ks8uFzLxmUcfiubohP8enzJCfuhjWhPiFv6/VyX0cHaiskaVZEjrU/T9HOlBb02cu+X6AhG1hXmtsl/1+KsINi987j96P88h/kil6GMptLeXBZGb7SpmcLXJ7ZLJyYkxtNu/LMIhnC+wZqkz6Xz12v92ZVLY/m7L1FLryMzWeCwOJ0YR92Fo4XumF1QyjJnfImY8RTA4gV83+Zwq1g8xS+NhIIkuydG0kU3nUitTkwNR57Dc1y9KZNJJhkD7+HFJytKGxLKfGMEoekwUaY1JyzHa5MvZCU/tUFYm5fB/V/dM3/hu8llyt8WSOgckUA00qtloN7+F4FmPG9BAaGLZeX6mqfePF+6XUs8emMxybmvVYG5he5RL1PJrR0USFRzdrMX6qvZUPHJhgLDGH9xegkGXPYIyJkdpC8lZiact9bGlfdVNn5fTnd388PxRn32iCRLFgdcfMHpRSrp/lINZzrj7CTDrPc9WTaIZBW+zFijDgZlUfFwthoJ0tGAbKPR+kdeIpPuH8r7zn9a/HuVTyrGcZsiTxgcvXEXnVB/ijwn9HGnse3/dvrVJiEwgEpwNWr0OJ8bj5g1+SBperJmBymo6EgTMfY+2JX1Z8p00fxZGPV+Yh2ZC0NXJgKtV8+F61JHXN94Mvl//unnwMqTiQURJD7BtL8vKJ2jbWk0O3Got248JUVjPDEYuKlqmchis3VWFgHCzOaqfzGoMzaTTdIJnTkAoZ4pna/gjP7MWXPFL+bJVzn0v9DUyBDTslzYHJFFOJNMQaDzatxDJ5Dj7yA1Iv/6qp9WVDN0UJDIN4Jl8O78sWtJqB32OHp2pGkasmHqEt/jLtMVMlsvT1Ew1CZQuawcGxWU9lu79Sta7aICjhzQyTHH7Z9rvZbYvnpWdxDj6COrYXSc8zlsjV5PkBDD37y5plYPb9XHgzI6QH7D2y6tR+M4ewDkpiyGyvrlFv8B9tENZY0Y4Tj+MvhmFWo+kGewZjZC35b1bhoMlEjsljZo7XRDLPVIMcVFnL0jv+gEUQw2y3XbiiKdpRO9FhZWAiVQ6prvbWWwV7JAxa4y8Tz1a/R2rvk5IndriOB7Dupja3XF7TeeLIdEX7FT2Pax61/ezeQfVMvVRx8qOnGJZ8ZCJVzvnTDQN/6ijh+IsEU0fpnJ4VbCqFms5k8nXf14uJGKGfJSiPfY62w3dzB2/i5jf8IS0ex3I36YzjNdu7eP0b3sMH+Aj5mRN4v3cLykTjHzmBQHAaUecXvzQL7c7VhtVFDx/FnZ/Nt3AWTCPOlavMwbAOpK37fWHYXs65uinPDcYYbSAssKeq2LE7N10eZCijz81KV2tZXjhRqxg3lziGlZyms28swZNHp+mdeJBVQ+bgvGfycbqmniivV8oLOTieZDiWY++QKcaRq5MvFEibk1560etlpdl594M2aoyGAW3xl3CMPmezhWG781II49HJ9Lyk2nVd5/BEmr3F6xodsQ/1LN0z1YcuhVJWhz42w3giV5nPVMfj0jn9HMdffsL2O6pbVfxTmT5M7vgzDM+YXp5qEsX+qvbaHZ5IEcsUyGk6/vRQ5X1m6Eh6HlnPcmQi3Vj9L26fh1US5hg6+Cz+tP06zXrEAPx18r3y1fKCgDtfaWCULllB18uKrckaY8g8hqNQ24eV106fnfQp7ssAAml7A7JEtlD/vnHnJgklB9B0veyZC6SP4s2OlBpgi5Sy926OJ3KohWqvbSkXdnZnumEKe6iFyuvQM/l4w3Op2K+N4TdUpxj60HTadFrYiIg8PxSnPfZiOYrCla99F6aymu37erERBtpZgBq9m7anP8td2hVsuuUjrG0TcvpLxc7eEP/t7W/jT72fYCadx/f91+EYemzuDQUCwWmDMbK3XNML4PB4CjDKIgfVtMVnJ2rcRcPMVxr0NCA6kqgThla7rFrK3ZceqlnHlZ+p8GKNxM3B8MtWI0ErlAf/3swJDMNAjg/RPXIf4djz5dVK7VInapXfNN1A0XL40kPFgaZRHiiXBjyynsc5+TLZfHHgbhjEioPV8UYKl5heg8PjqfLgVtYbrw8GLYl9KFoGz+QLFaIKowlTfVJtYIBaB8Z2V2M+M/0DRXEUqwhC9TUOpI8RtHgKm6HZ4NfxRGOPbL0cu2aOVzoLrcqDVuuRmSWv6Tw3OFP23vaP/SehhCmY0Tn9DGtG7yOQNgfLJc+c3TWQjj5SVyoeqFB2nC/poselUUbIQoOP601GzEXf+IO2BoxS9SyohVQTJSlMSl5xa185Cmlc+VjVepXPRL0ahVZJflduCgmDdFH8aNgymeSYPsTqsQfoG3+w6bDbZpjvNZ+vV6ym3MgiIwy0Mxxl+En89/0Jj+tbmL7601zQ37rcTTrjWRVy85dvu41Pdn2Oo/kg/rvfgrr/J8vdLIFAMAee9HBTNavGJ+0VyCrEOSxI1twLSS4ua27w0Dv+EGtP/JKO6acpDQMlQ+f4SKXXZvVYpUR4x0ypYHLlgMeTHaN3ojKnyFr41ToKLalBlorNBlK1AhPRY/UV5Mq1kAAlXeld7B+9D23kJZ6JmnlxqjY7ez4ez9UM06y5Mpni3wVDR9HS9I/+mmCiVgVvYDLFcCyDKx+nJXGI1WP3400cpnN61lNWnbtWjURlD8pG7aBsrpl+67nZKk5a7oVDY6mK+2ix9DNULYMvPVSRC5gu1J6L38awB1PM5siUeR5SPoWaMu+JSoXB2b+TuVmj7PB4/T42DAgmByqWBVOmcVrK/7LzYtghVasoF0Vv0k3WKqvH/tGkacjPE6nuxbOGB9euY53QAdPgPTaVpnXgp+Vl1ntENnTaZ56ruVdkPUff+IOE47WTKJWtKW44hyZBaVJpvvekKzdFz+Tj5jNfvNWzeb1scDvH91rWnktXX8eZn6nMDdPzqLHGnkMrViOwNJmw9sQvOTo1tydVYvZeOrDEXjR1SfcuWFbk2FHc//4ujmtt/Oc5n+E9O/qXu0lnDX6Xyodefw3//Ov/x/UvfZALf/UBpmLD6Be8d7mbJhAI6hCefIZAs/V5qnAUmg2TKg2CjIpB7Fz4MqMYHvMnO5QcgMMDqO1XNNxG0dK0VhsuEuUkesOo9XbsHZqhMlOpMbmCTmtiX8N1pNLBbCgZBHbhRlYmk7Xfzwztw+Mw+7Om8C4Qs8knbERO03GrlUVrDS1PfnoIZDfQ3HU2qga64ZhlgFwqRl6YHdx5cpPgXtrhWNfUU+YfQVd1U8rMNAghPD5VNAiKc7zuikF1JZph1M1drMabGZ4zP7NpqgpBOwcfIZErlBVR7ZC1LA4tBZ7Gux6NZ1jdWruSARyfydDqWeD1M8zctS3d/rpeuIlkrqZAczX+9HCNEI+im14qX3qQtLOdvFuvMX9aEvtpKRwhBzjnMIRL9+y90TFusantrNSZoHLUub4vDsfZ2OHDZdFDaDTZUZ3fO9B9I2tG7kEyDNySD1f+nIbtn21PkoyjDaCq3Ef1dIxJQTdQZQnfzH5aFmCoLxThQTtDkbIxlB+9nVw+x1f7Psm7rt653E0661Bkifdddx4vXPlV7tEvIvzYx9F//b+aDjcQCAQrk3i20JSnzQ6ruMZDB+vXArKj2rvRN16rrmdl1cQjNksrh4GZKuGBkbh1AFJ/kJ2yzID7MrPhmuOWAYzVg1BR5Nuy3OoJSDcIGbJ60ArFwX88XZiVGG9yWt8qj17h+5EkjkzWXtOxWIauqacrBpnOfAylQZ23ZmhNHGiqhpak5xueW//or2tyd0rY5allbLxmyswAUD/EyxqiWDLoSzaoXRhZto5Euh1Wb+ZcHJ/KmOIqdbrDeezBcl9oukEyleLYVGMRi1WTj9Iz+XjZy2YXGtyITE5nMpHj+PQcYhmYQiF5TbfNFU00CANtll/vG694v/SOm6GHsqHTOf0ML51IVKiDqjOHaEkcnNcxAqmjtpMh5vHspfMblQGovh+roxAa1cST9ELlO8bGu22HWbuyWH7Bcv/WU6zdP7ZIEwjzRBhoZyJ6Aenffx9f4gj/EPxL/vCW65CFnP6ycfPOtUi3foU7uZGul75K4cfvaxgrLxAIVg4v2SgbZvM63VONRBSaY6FGXtP7t/FKWQdkBtTIuVdzfCaDNnWkZqA0mcw1UJWrxTk5O0hz5yrrapW8A9YZ8hNV+7Eey+64rlx9NcN6DE1ncFrya2zEB8tYB3+rJh5BydU30Nri0brfWcOr9g7OHbonZybmLBngz9jUNquD1UCV9TwU0mjDz6HpRlmevxpriGKhgSvH2n31hhz1w/5mkY1C3bzCE/GsWXi9SDqvVQzyu6Z/C8ALw3H2jyUqw3dtKBkEJeO8Y2YPbbEXaa0KM9R1ONKgHEazRmn1hMhiEkocrPWYV2GV5XeO1feC1iMce3HWG2vBnzqGvASTz41UPleP/drySTrp/LV6wjv5orCKnRDMUiIMtDMNw8C498OERx/hM8738843vKXCfSxYHi7oD7PlLZ/ni4530DP4C/LffqNt/RyBQHB6YKey1gwlJUJVy9TxcJ06DMNgsGrm36FZB0SmTPv+0XjN4GVwJr3gHIxS6NVCmI93phlKRmwzhoMVuU44F4C7gWiINbSxGQxMQYhmmK/IQSA9yPThp4iOJHhhON5k35ZU+KrbaVQM/k8GyTDoH/217XeJrFZhZO4fTbJvJEm+oDOdzlc8l/MZsFsNymDqqBlGvATUGq6LJ4rRmmi+aHh1Pb35Uu2ZXGixbAlpzlIirtyUrWez2iBsmyPX7mRI5ArzL05+koiR+5nGk1+hc/+d/Au3csPtf0yLV8jprxRWt3m54R0f547gn9M+/SyFf7sZIza/cAqBQLC82IWILQRPdnzOvKulpmAzY9w9+duaZfMVmiupQ5bQDaNiENYxM/+Z+7lYgOp8DY29LZUHcE/V95KdcopNOziWbLqmV4mR44fmtb4smbXbKg9vMBLP2uYJWmmfaT6csR71rtGBsURNoel6xeFPhkbGdzNUPxsljk9l6nowlwJnIX5SAjSLZYxrhlHX616iZ/Jxi+iRPZI0nzxgWDvSXP3CEiejArpQhIF2BiEd/A/anvhbfqVfRP+tf2ub0CpYXgJulTe87b/ytTV/jy9zAuVbN5E98eJyN0sgEDTJvjp1q85EfJnRBeXMVotzpHM6Y/ElTq5fJEfEYJ3BYnts5b6n3fmpsktrsT2M1RyfzhAdSTJpUaPcOxhnNFZ5fe3CAevVIVsM7MJeVyLJqgLszQikLEVknWQYDMfmzpsr0ahUQnWNxfkw1ETuXjNUG+dnAsJAO0OQxl7Ed88HeFHvZ/ra/8POPiGnv1JRZYnX3fImfnH+VyhoBUI/fB2x/Q/MvaFAIBCcYqolv0sshXdiJTBxClXamkFvlBhX5GS9OvOh5OWZXGH9ZCdXPx8aiVFUIy3ivW+Xz3WqSGabjwY4MQ9jbjlY6ppkw3N4+ZYCYaCdAUiJEyg/ejvTuocnLvoCu7cJOf3Tgd2XXc3z136XUaOF1b/6Paae/v5yN0kgEAgqqFd825ObnwLlmUYTdtOiEB1ZHgW5042TdjLN43quGb33ZI922rEcIX4A6jxzNs8khIF2upNPkfve23HlZ/jx5n/gtReft9wtEsyDHVu3M/m6H/ECG9n4yJ8y+cAdy90kgUAgEMzBcsyoN2I+AhECwenCXKVEzmSEgXY6Y+gMfu2ddKeifHPVR/idV12/3C0SLIC1vb0ob/khDyoXE3n+00z97MOiVppAIBAIBE0iCgkJzjSEgXYaM/rzj7F29F6+E/p9brntnUii1tlpS09biJ7f/QY/dt7M5oFvEP/BH4haaQLBHEQiEVckEnk+Eolc12CdNZFI5FeRSCQZiUReikQirz6VbRQIBEvPKYo4FQhOGcJAO00Zevhf2T7wL/yH+0aueNNfosrCODvdafN72PmOO/g337tZP3oPmW/dLmqlCQR1iEQibuDbwPYG60jAj4EJ4CLg68API5HIulPSSIFAIBAIFoAw0E5Dju25j63PfIyn5B2c/75/xu1Ul7tJgkXC53Jwzds/xpfDf0FX7Dn0b96CFBe10gQCK5FIZBvwGLBhjlV3AxHgvdFo9MVoNPop4BHgPUvcRIFAIBAIFoww0E4zjhzcy/oH/4hhuRv/G79GyO9d7iYJFhmnKvPaN/4RX+79FJ70MOqdr0Eef2m5myUQrCSuAH4FXDLHehcDz0Sj0bhl2UNNbCcQCAQCwbIhDLTTiGODR+n+5e9hSDLp136dtnDncjdJsEQossTtt72Zr66/g2w+j+f7tyEff2S5myUQrAii0ej/i0ajfx6NRlNzrNoDVLugR4C+pWmZQCAQCM4WjCXMfhSxcacJQ2PjuO9+O2GmGbj+W7T3bVnuJgmWGFmSeOurb+A797fw6uf/mLU/fhvx6z6HFrl1uZsmEJwueIFqtZ0s4LJbWZLA6RY/iwtBkiTRdyeB6L+TQ/TfwhF9t3CiE2leuUT7FlfkNGBkJkny++9mF4fZd/kXad8konPOFiRJ4i27L+b77q9y4ZN/zCvv/SOmEicoXPC+5W6aQHA6kAFCVctcgK3nzTAglykseaPORJxuVfTdSSD67+QQ/bdwRN+tTESI4wpnPJ5h4Nt/xKXG0xw8/2O05H8qKQAAIABJREFU77x5uZskWAZuv+Qc9lz+z/xCeyWtj/0Nzgf+l6iVJhDMzSDQXbWsGxhehrYIBAKBQNAUwkBbwYwnsjx650e4WbuXQ5vfR+ulQnjsbObW89cxft0X+HrhBkLPfxX3Lz4gaqUJBI15DDgvEon4LMsuLy4XCAQCgWBFIgy0FcpYIstP7/wM7y58h8HVtxG47iPL3STBCuDGbavw3fQpPq29lcDhn+K7662iVppAYCESiXREIhF/8eMDwBHga5FIZHskEvmfmMqOX1m2BgoEAoFAMAfCQFuBjMaz/PDOO/hg7kuMdV2J8zX/28xeFwiAqzZ1sP3WD/Fn2n/BMfIU/u/fhixqpQkEJX4L/BlANBrVgFuBTuAp4B3A66LR6MCytU4gEAgEgjkQIiErjNF4lm995yt8PPd5pjsugtu+CopjuZslWGHsWtOK+/Xv5313tfL5mc8S+P4tJG79Jlp463I3TSA4pUSjUanq89qqzweAq05lmwQCgUAgOBmEB20FMRLP8s/f/jofy36GZNs56K/7Bqie5W6WYIWyszfEu29/G+/i40yncwR/8DocRx9Y7mYJBAKBQCAQCE4CYaCtEI5Npbnjzm/zd7lPkw1tIP87d2I4/XNvKDiriXT5+Yu33sr7XZ/mYL6N4E/fgXvPvy53swQCgUAgEAgEC0QYaCuAA2NJPvudH/GZwt9BoIfs67+D4W5Z7mYJThP6Wz188q3X8dHWf+C+wnkEfvNRfPd/CLT8cjdNIBAIBAKBQDBPhIG2zLwwHOP/fu/7fEn/axy+NtK/8z0Mb8dyN0twmtHqdfLZN17Mnf1/w5cKt+B94d8I/OTtSJmp5W6aQCAQCASnJQG3kGoQLA/CQFtGnjw6zb98/7t8mb/FGegg+fofoQdWLXezBKcpbofCJ197LqMX/k8+mPtDlMHHCXzvFpTJ/cvdNIFAYMOqFveyHl9ZASOAuLdvuZuw5CQ8PcvdBIFAcJqxAl7PZycPHpzge3d9iy8rn0QJ9ZF4/Q/RA73L3SzBaY4iS7z/srXsuvkPeaf2UZLxSYLfuwnXvruXu2kCgaAKj0NZ1uOPtL1yWY8PoMnOJT/GSOsFS36Memiyg6yjddmOPx8MUc6nljpdcqzj6lPajB29wVN6vIWgKBKRrpWlnTDt38CxjtNTxFcYaMvAD58b4oGffIUvq/+A1LaB+O/8AN3XvdzNEpxB7N7Uzh+/7S38gfsfeSa/muB//Be8938YCtnlbppAcNqwLnxyKro9IdNDNtB9o+338jKPh7OuMNP+9cvahhnv/I+vKPPvuBnf2nlvsxhkHbP55Atp91Iz7d9Q/lsyjEXdt0Nd+iHmqTYqvS4FQ5LQlKX1fruczfWdJq+cMkyyJOGaxzWfCG6zXd7mqz2nwfBlC2rTtH8TmrJ0augFxbVk+xYG2ilENwzueOAgE//5OT7v+AJa9wXEX/d9DG/7cjdNcAayLuzlH3/3VXx78xf4cuE1+F74BoWvXo8ydXC5myYQnBbI8sn9RLZ46uevhLynJrflWMdVpF3hmuVJdxdIEtP+zaekHXbEfGsw5Pn3g7oAQ2cqsGXe2yw2Hf7ZwVzSXTspm3adurGAu2gAVHswW7yVg+OMc2Hev1avg7DNQHux0SWVga7rl+4AFpt1R2+Qje0+jnTdYLtqwjObojIe3M5A941MBiINdz/acv6cxwUw1IUbAllHiKHwpQvefr5GYNy7eu51PJURY5OB2fdQX0utQZV3BObVhqWm1CfH25fOOycMtFNEtqDzkZ88z7pnP8FfOu4kveFm4rd+S6g1CpYUr1PhQzdso+01n+BP+B9kxg7j/84NOPZ+ExZ5tlQgON1Z0+at+Oxzzj8E0WOZ+XbMkeTVaPJ/W0+ALd3+BeWJ9bXOzu5rigcDGZdjYT/3O3qDOJuYFVeK7sD8PGp3ZhxtC2oTnLx3c7HRpbn7qNM/awzl1FM/4MyrvvLfdp4OQ5IqvLoxbz8dfZts15uL1a0e5Kr4wHNWLdE5SzJJSy5jtZF5MjSaDOgIVBq3Gztm+7f0cMd86xruP+WyF4Wr6eImjSS75zzjbKswxOt5ruqh6I0VoTd3+So+G/XiQpl9P2YdoYrlhSX0coE5YTAXdY3lKgxJ4ljntWZkRBPP/UIRBtopYDqV58+++yhvHvgw71LvIbXzvSRu+CdQlzdBXHD2cNXGMO995/v55Jp/5on8Bloe/Av0H/0eUmp8uZsmEKwYwv7mZqkngts40vUq8qq35rtNHZU5GBu76g9KGw1zVVnCuUAVD7daa1j6XM17qqyegGZRiiPKkZYLm1r/aOe1pNxd8z4OmP3mncf5pJ1L75nKVQ04S2gLDIEqea7WtXvxuRYnVzHrMPOYxkI7iLXuAECXHOXjBLZWeqImg9voa60cfI+0XlDTnzm1ubwjeRHCEes5tbPF/lIVif7WxRvslyYerM/EVRvD9ITchNfsLI/Pe0Juwj7nnB7HzmB1zqV9n7hUuWKiJd/1ivLfBUt4pVR0tbUHnOzoDbKpaCSmXe343OZ9k3FWToQs9uTAfK7rpg4/Gzq8VJ+3Jtc+J8fbr6j4PNpy3oLaB5BomTtSQPKFGQ+dM+c1nPE2NroXC2GgLTEDEyn++s6f8omp/85udS/xqz5B8vK/WlKrWyCwo93n5O9+93rGX/Mt7lB+j5bhB3F//UrSz35XeNMEgipWn28fxgSQdoYxJIXGJpaJq309V29qZ/Wu1zHts+RbGTS1fYltPc0PqlKrLiHnCJQ9HaUBR3XokfXo1oFvocn8mmMdV5VDlToD5gCroPoqZvE3dfpst9VtPAJz/SxmHcHyucgSuNyN21k2bCQJt41nIenubHzARWDSb4ZWequ8sXaDQKvnoWQEBeZhiM7jdiLj7+dIz43F+9g8Tm9XVzmyTlPsxVvSrg70qrDUofCljLS+onbdllnvW61hsjDq5TilfKsxei9iyxKJVFiNHLdDYceqIEbHVjzFyRDz+krlZ6eeF0mZ4yYv5Uo6FZk272yfGe5Wesqqr7P7zivm85VafS26t6NsLMU9fWUPZtrVYWsAVVDcZb3rPtjeTA5Y8e6Zw2DzOVXWt1e/Fyq3cTlkCqqPofDFjIXOBSDl7i73jyY7aPfXtjVkCSsPWEPMpcYetK6gi8vXt5Hw9JGxiPpUT1ZpipNp/8aG+1oshJWwhDxwYIKv3/kVvpT9c3rdOWK3fZfMOe9Y7mYJznIu29DObe/5OF/d+q/s17rof/iDTHzjjSTGjix30wSCFYPXZ3obGiXrJ9w9Fd6Nox27bddzqTJeb4DpQOUsrlJHJSRYHFjoNjUxR1t22m5jDZUyUBkKX1bOlYn51jPRczUTwe3lwY4uVQ6ylaKFZlVhm8uTpike9GLoVGfQzbm9AbqDLvq6Zj1j9ZQqd9qo0vUE3RVeg2oKiofx4LlIgITE+rC36vvKbSuPbfb1qhZ3eWA31vIKWy9oiZLIhTU0sNH6dhiyyvZVgYrwt6Mdu8k664d32hk7Vtr9Ttv6XNXGXMCtloVJKsMSZyfkSssNV7Diq2Md1xTXaDzYHg9uB0km7ao1dtMts/lXJcOhmdzLRut0+F1s7QmwpirEdV3Yx8ae9lpvzrora/ZhDfHLOFuWSGhj/t7CwfbLmSrmhHYHqwwqScLvrO2XkdYLCG/bTV/nHF5iS79Y74X+Ng9rw17bCQwr1e+LEqXcNkOSykZgzNPfuC2A4msc3tzuM5/RnKOFpKc5hfM2v5P+ttn7osfah81cjlK/SOBQS89N5fvrWMc1p8zBIgy0JUA3DL760AGmfvYhviD/A3LbBhJv+gX5VbuWu2kCAWDOAN5+7W7Ut/+UH4Q/QH/8abq+ew0v3fXXJJOJ5W6eQLD8FH+rN7X78LkVhsIX1+TeJDx9qBYjy+0xB+9rwh5bJbJqVBsDbXWrhzVtHnRPGC24pmJgFXCrZCzhZfVCfqpzxvrbPOhFIyPpXsV0cDOTFtGMeiF0pbyVZv3rEhI7e0N4m8h3C/ucrG+vNHZcikRrMMBQ+JKaPJm4t4+J4DkUVC+F4rlowTUV6xhVA6cWT+01UGWJVaFZQ2641f532aFKbC0aq1bP54nWVzLSegG9Tdawa/E6aLO0I+NsQW8i7HHn1m01uT3VbbGyucvHmjYPW4ue1o6Ak3Vhb9mIzTjDlG5qyca/YxRzB7uDrrKB1OZz1HhDrIb1tH8DiSYEIQCyXrMWnMsSfjsW2lF3fdNTYSpMlkIoU64OWjwOHLI0a4gV/z+nN4TqqfSeHW+/gjU9tZMMlWdv5hMNt5n3wWjL+RW1+Zr1YBqqB611bs9KPTvB5ZRp8816hCRJQWvdgKJI+KuMcbdTYUPR4NdlJ+1dq5FsvFbjnZc2VD/sDrlo8Tho87lwlcKp60TTaLKLNp+DjNPUTUg72xgKX0xO9dfeS5LCYPvldY8LoDsblA2QJALbbuDazbVGZymkM1ln8kiytMatKly+wTQEw+1N1CK0nLtseTeX3qcLFcxZKMJAW2Sm03k+9YN7ufGZd/MH6s+Jb3sHydvvFgWoBSuS7hYfV735w7x8y8953nMRVw59Bce/XsGDP/tXYunccjdPIDjl7OgNVtQckiWJkNtBztFSk8je6p/97O49h11rzB/wkNtBT7+9elspRMeMcKz8CXaoEq1eBxIShbZIefBZKnRcLVueslEC7G/z1AgFbLXkwW3pDrBzx0UYRa/BdZEO1q6uN+PdeNp59TxyfezCkRxVCUUBtwPDESDnCNXkOfWv31oOi5TQzf/bKnNBrNsc7bwWr1OpEG2xsnHtWlq9jgpjadU5uxltOQ9NdpDw9pPddCujLeeR9PQW82ZAU9wUvJ20dq6ecyJ9e0+Ai/pnhcC04GpOtDZXe87jD+HymnltfS1uAh6VofClNV7YEmrXOUhOPw5ZYkdvkJ5gpQE5GjqvwjCpl9/oUGTWtHq5fksHF65uQfevqjAKJUnCXzRaSuF1AFu7AzWGRMg9a5jq4VoVzaRnFRlnK3FPL96qSYKYd225RIGmuDjWcRXn7poNO/a7FFqrJ0FUD4Xw7HNXUH24XU7aNs32ecirsrW7Ngwy62xloOsGUu6uco6RpjgJuFTzOHUMl9J9bWy8cdYL2YCaHDpJQpYh0uHn/L4WSs+c1rIO3Rlke3egxlMMzQkYGe7WptUP7ZQTS2zt9tPmdxF8xZtIuM2xbEH1knPMtreafFVeYrUoCNSf+NHdrRiuEKrNPSoZ5rPvDzRnLPmcKjds7STY0l633EltWyS6ivnIGUcrkmyWyBgPntPUMRcLYaAtIs8cm+ZbX/ssHx/5AFscY8zc8GUyuz8hxEAEK57VayKse8932HPl19Cdfl4/8FEmvnozP/qPXzGdbqzgJBCckVgG7u0+J1dtDJPwmDPrqtN8p1+wdjas0NW1pcJzZafQ61RlnKt2MtL6Clo8jhqPT2m2PrvpVgxfJ7q/B0f7RiYDW5EkWFUd9mShlBdUb+BtlxYiYYZZFnouIqcGGQpfTGrjrTXr9YVcFUafnbFlpdBWaUTs6A1WeK1Kx7biVGXyvZeQX2UOpguql2Nd1wJmfk+nRcAlUwqns1yj4bZdxD2mNyfnCNjmuFlxhNeV8+ZKaL5uUu5ujnVey1RoO0iSxQi2tFiSKIS3NjRf14Q9KFZPD4Cs2oZHzZXz51IV1rV5aQvbK/4BoDhspdhLoWuGrDLt30TS3UXS1W0JuZToDLqoGaJKkumVkaQKo7CeR6m/1cNExyUVy1q8Drb3BFjX7q0MNyty2fo2TrTtIuHrZ6MlJyke2saMd23FupriQVXk8v0hIbHaxqjQwltrlnX0bUFbfRlOj5c1rV7aA76KXCXLSQOmYXe0Y3e5DatbPFwRsffABLo3NaV06nLK9Ld5aPU66Cr2RamAesV9KEkMdN1AoeNcdF8xVLjqnsnUUX60ComAxKYOf22oJGAd+uf6TZn4Urh19SSUz6XgUGQu6m/Fpc7qchrFfZQUM3XZUbZha/yzkvlMNuLideHZd0yDnPgp30Yc4TWs3zBr8A9030jaFSbeXl+F0c7DGPf2Vb7LLP1cUuBNelaVwxwlDPxulUjnqSnGfWoKsZzhaLrB9x56hq3P/jUfU55ipv0Ckjd9AT3YnOtfIFgp9Jx7HWy/moOPf42tz/4jr9z3bn758i5e2vg+rrvsSrqDYrJBcOZTaNsMkkx282249t0NmGHBM771zHjXct2mLoziIKJ6MGIoDurNKu/eZHp4pLY8zuMHameQq8K2kGRWb72Ybs1AOTS7zyn/JrLOWQNwY4ePSYeXUHgVXiNBo3L0BrOhlaWBIqqbkY7LyOU1wAylxBLpHHA7iLgd7BmMAWbo23BGYVXQxVPetfj0eMUx9OBqM6zSZqCVdHcx7d/IBsuydr+TVSE3WV+lquMVm3sYGDElsiVZAQoAxELbyK72geJgKHwJuqRSUH048pXtsCJJphHXqx4A8oDEmlYPbV4HB6VXMpOFLstlCxa9Pxs6fKZRYknRVaRaAzzmXYs7N2UeS670HmneLlRAC/bDhDnh1RFw0pd1c3wqw2D4cgI+B+6hJ83tyyNd8/987yXozgChGJyIVV7dzqATXXaiBdcgx4dmT7a4rYTplR3A9P6NFaXErfmPnXMY3CXO7Q2Qq/LcOBSJ1qKYheybrbdXqm8nuwME5AR28Rgl4ZScGqTQugkGnwIgFVgHsayt0IbunzsaKbvh1RzLnahYtm3jBhyOo5CdwXD66Fu3jTUzAzw+NnsMVZFQJIlsQUdXXHXzrqwUus6j0DW3umDEou7aFXAxEsuSrjK0yuc7h8jGdGgLhrIXiFV+4fCQ3fBqpqd/S8rVidepsKUrUHPP5Byznj7DPeuJ8joV0tLsvbC9J2Cjmln5TDtVhWzneQwXZLrS+4BUxcSTokj0bN/Nk8fjBDPH656Tx6mwqcOHPkc8ta64WLPtShRZwvq0j7RehMvrgZmXGu/AQijUhpwcAyDWuYtWpRTSLeFzKoT9TgYAp8tLOj8DwEWrW3CqMtHRpU8FER60k+ToZIpvfuPzvH3PW7la2cPkro+Qe+OPhHEmOH2RVYKX/D7Zdz3C8W0f4CplL39y6N0c+9pb+dLdP+fAWHK5WygQLClae4M6QZKMIkvl8JtY6zmmyEBxUJJbfxO59a9ufICip8Nwt9aELVYjS1KFcp0hycz4NxRziky8ToWekLsspW+dLd5uo/6oKjK7N7Wzpc5M8LmrSuFIlaOldRfdzIZXvAq3qnDeli20eZ3s3trLllfWnm+Lx1GhQlcir/rIq4E5B6Fgiqvs6A0S9jkxXCEuWlM0iiS5HJmSc4TKOWmlgWG+6AXQWtZXKA5mna1ortI+TA9R0O0g4+4si3Zs6w7Q7ndy7ipzELux3UdXlaftgtXmPnxF0YaB7hsblwxw+shuvg3D3crmYp/7HGq5fwxZpaPVEh5X1TWGwwcOe3ESn0OhY+PFICuzHqDWytpl1lBUlyqX76fS8rkuxWXr2+hrdVfk95S4ZnMH5/eZ98uWYiitxymXc+51V6h4jg5ya68lsapWtMPvdqB1bC9/LhRH6RlnG3FPb01oWWmfdVFcaHPU1dKKhp7VeyUBl6+fFa9IeOY/jrNzYE/5N2LUUUecPTIgSXQFXDaCHTVF0citucY+ZE9xkQtvber5KhOazbmzGsWKLCEhkbcYoJpsPnd51UdPyM3WLj9acA0F1ctEaAe5/qvL4dPnrAqwtcuPL9zHeX3B8vvGvji96W02J48aW2mluYVgMaTWmg9qyCoxrxmyrYXW1Gx7rOMqYr41tAecRLqCuIvPgitkCRcvvke61u/kwv4W+lu9rA176fC7cDSoi7fYCA/aAtENg18++iTrnvoYfyY/x3hwO7HX3IEenrvWgkBwOmC4W3Dt/jDJS95P/Ikvcc0L/8pNg+/lF9++iH/qfDO7Lr2eV6xusQ0dEAhOV0ohP82ihdZyTO+iQ25u9hvAcAbIrdkNhs7mxCgn4lkmEs3lfFaripXbEViNOvFi+fPO3iBSccBnRzPFpw1JwVBd6IHVFNoiOBVz4JWXLih7MuzyRCr2oTiQNNNrFHCrTNusM95/M+FG8ujlEKPSZ/vVCqqP8JYrODJpDoYLnTtwuDbBUByvQyGd02g0+JMwjRa73DrrIUv5P/1tHl4Ymp3HT3h6CAZCrNGPApDrvbRmP0bx+KXbpGRAnr+6hcnJIJ5shk2rwlXbVJ7wZO91pDIp3Efur1zP6YfUGEYdA8bvVrls3awBsqXTz1ZfGGlIKnuE8507MTyVCnt+lwrnvbHsmc05gjipDYdTZYlzVgWQJMgXr1mh63y00FpweM0zd6sc7djNqpALWZK4sL+lJmxybZuX8UQOJJmJouqoFa1tM/Lwb237plkMdyuGM0Bv/yvocIT49T6zJmjF/SxJ6P5u5ITpjdNdISSt8XO6oyfA/gPDrO/wsbfoZJnxbyS3oRN55giGpw3nwH3l9ftaPfjX30QhNQIOL+f1WXZW913S2IDZtaaVdNEb3gxG3y6MiTGgdvI13/PKCg2FlLuL4bZXknW2cWlxEiNTPJYhq0XP8ihQWR+txeMshw3mNt4MsUxlG1whDFcQKRuj0GEvHnP1pnbSOa085nBtuo5IxMFAYvY4uY03M5kfZSoQIdJZm6OrKR6C/ecRVjrRW9bSMrYHV6eMbIkOinn60UJutJaNhBUHiizjc6rs6A02VaR9sRAG2gI4NDpN9Gef4Y3JO0FROHHRR1Eu+H0MeXGKSQoEKwnD3Yp05YdIvPIPST35JXY//3VePfFB9v54Ld/038bqS97ClZFVi1KEVCBYduoYQPWIdPoJuFQ6/LX5TCp7apQGSxiuEFJ2BkWWZhXUTgLD1wkzh8wPklwTjlyKmmvmOTWK+SJ5xUdufa0K3HwiRAyHH0kzQ//Whj0MxM1+avZtkV+1q6z4Vm9Y6lRlVrd4kCRob+/EmB4tf9cb8tAb8hDPFHj6+DTennMwxp+uKGEw3zKQpQGiIkls6vQx6VGJpQuMh3ZywdZOXPvMUEPDVys9XzqWJEF24y30rdbwpgsosoTXpSLnJVSZogxKZcPU4uy94vJgZK2Ggrleof0cdE87eqAXigYMmBMCu/vaa8o6mHlmlfee3jJ3Ed6Efx1pvYVNq2vDDavVFZFVjKpyEbriAodp3IUtyoXHOq7CkBQiPic3bO3knpdGWTIU06MHgKbXXc0ohTlKMvk19mU0rDgdDnb0Bsm3eMsGWgm96NExxUTM8MTt3eazprnmzmvK9F5KIr63XBKjbhtUuakJmBpsHko7b2WjEhG1+6zMK7M+d1lHK363Ss+2q0GSyPVdhpyerJgg6Gv1cHwqDZjhtC7vbOjwbJhxquawhqTYGrhdAReR7hAwe14eh1KefAj7nEwkqQhbza/ahTJ9CMMxe426g66a0NHFRhho8yCZK3DPfT/nqgOf5B3yMQbar8Z/06dRgs3VaBAITmcMdyva5R8ivuuPSb34A3qf+gp/mvoc4/f+Cz9/4Hqc572NKy64YGE/DALBCiW3+kqknOkl2dDhI1SlVKfIkr2aocNLdvNtc+xdqvmU3fAa2zWz628AXYeD9UOM812vQPYN23pQSmNzO2l/MPO8hmc0VFlCD/YxFL6EnI3y2slQ6L6QhDa/gsW6v1acwXoGl61vw6nMPSANuFWu2mjmAOZ81zRc145sxw4Gjcpj5LvOR3H6ucTT1rQxUTbQkEBWCHgUAp6ix69tM470BLonXLWyeca9RaGVVSE3e2IztTuXFdM4q0ILra3bP4a7BUNWTdXQeZB3BMohZgvCxire3t9VtxB1PbKOlqbDzgqdO3CMPIvRSOLdZhvD6UP3Nghhta7fcQ7IjuJ9O267Tm71lRwr5Qs2Qclr423pJrQxQJ9FcMdW7MTCYk+bOlWZXKG+QWslt/bacrij4W4BSakQEMo7Aoz23URvqGhoKa6a531V0MXxqTSdAVfdyaXugIv9Y0lb0Rgruze1lyc56nHB6lBNHpzh9FPorPTq7VgVZEO7xsOHJhvu72QQBloTaLrBb57dg/+xT/E+4zdMOTsYuvL/4dv6mqbrwwgEZwwOL9rOd8CO32Xi2ENkHv0Sbx//IfKTP+DZJ7cwsvY2Nl/+Fvyh8Nz7EggWQCQScQF3ALcDWeAfo9Ho39dZ9wrgc8AWYD/w59Fo9Fd1d141CDA8beUZXavS3KJQKhJsXabUUR9US4OPBjmgirM8S1/NxnYfDkW2VdMDcyZ/XZu3PJB3+sPkc4UGjZ8/enA1DI2CMb8UmRJ2ni5/k3WqGuFzKsQzhYaDt0JoLfnJqYpl1r4OuFUSWbO/6hnZMBviaDdyNtyt5DbcVHdbSZLs5dBtOia7/gZch+4pbVh3nyhOM+RsHsiLPuw3qS4NYMVOOdRw+ll37mV4XM0Z/YYnPOs1s1A6G9twXcVpqwxZF8VVM5ivQVa5ckufrYjOXFjfQddstinMXYX1CMNtu3DlZ4OMq3MKS5/Whj2zRd6rIsOu3BAmr+nlPEE73A6FTF7DcFryXxUn2U231K48R/tbvaY3tRFuh8KrIrOeuTVtHnJabfuamTyWJIlm7H1JknCrMm7H0kXOCQOtAYZh8Oi+48w88Dluz92FIhkMRN6L/8o/xeE8NTKbAsGKRZLQ+6/A1X8Fk/Ehxp64k/b9P+S8gU+RPfxZXghchuec22g/99XgXOSBreBs5x+AS4HrgD7g3yKRyNFoNPod60qRSKQT+AnwKeD7wJuAuyORyNZoNHqEZadooCkOIIPRRAFji0DfvFAVmQ0NDExFlkz1xiKXrptnUVbFCQ31IxePxY6m3t4TpLclXxb9WAiXWnK76hrZzJZBcDeTA+j0IxXSttL8c+ZeqR7yvRfjGHz82agkAAAW5klEQVQM3b24BXZ39AY5Oplu6EFr5l5ulmsjHbaeX90ZrKhFuFBURWZrd4COohG4qdPH/tGlFcNajEgTxzxDo7POVvytXZA0w2Pb/Y6yumahfTttsRiDmQAeZwapczs5X1dNmShFllDqpPOUXkuXrW9Dm0uOcQnZ0tVc7beTRVVkrtq4dBPRwkCzwTAMHj08zuEHv8GbE1+jW5riSPf1eF/1MXyhfuE1EwiqMAKraL/2z+CaD/Jc9DFmnryTc6bvpe2x+8k99j8YCe/Cv/0WtA3X1+QjCATzIRKJ+IA/AG6JRqNPAU9FIpG/B/4L8J2q1S8DiEajnyp+/kQkEvkgcDEVwukmRtvGylnfJaY0yDYklePtV0B7kPk+HTO+dei+xZ/Fna/4j7H+GvIjNmFbxf3oHjO8MOxzMp7IzemDsfNmlcQk+lvtFQ0XiipLtPvmF3q5UPpbPThVme464i1W8j0XIWembGupliTq5Qb5SLqvm+ym19oaeCeDx6EQqSPqkt1wk9nmOqqTC6FeWG4jLlvfRsHGi1KPfkuY8vqwjxaPg0y+uVC+eswnHHCpcagyl61rI54tMJHMEXCrFR403deF99zb2GXoSJP70Vo3Nn3flHMyi9dJlaUFXbPloBDeipy1ky5afoSBZqGg6fxHdISjj36XN6fu5FZ5iNHANsav/We8fbuWu3kCwcpHkli15RJWbbmE6VSGXz9xL9rLP+PSscdoffAh9Af/gnjLNtT1V5NffSX5ngsris0KBE2wE3ABD1mWPQR8NBKJqNFo1BqXNwGEIpHI7cAPgFuBALDHds89O2G6NuF8ySgObHxOmYLhIxyc2zg8d1WwXI8MYCoQId/bOATolOD02ec/tWxETj9RLjB8Xm+ITEFraABes7nd1oBzqvKc4U5gKlgudi6st+hdqy5uPV8kSWoYyleB4pwtVlzFpk4/hQkPPqdC1tNgFn+RjbM5adBmWHzvZz1ONvTVrkTEfLlkbSsPHJg46f0sVqe5VBmn4mBtm5c+6tyDkowWnl8+okuV2dTpozvQfJ3UsM+J2yGzPry4ky3zRQtHaF7v8tQiDDTgRCzDv+85Tmrv3fxu4Ye8Qz7GlH89k5d9EWnTayqK7gkEguYIeN1cdfXN6Fe9ht8emeJrzz5G6Oh/cMnkc5w//UV8T/9fNMVNYdUu8r2XUOi5kHzHzrK6l0BQhx5gMhqNWnWaRwAn0AEMW5b/BvgC8F1MYTwF+P1oNNp8NdOlRPWYAg4t63iVM9iUwmJP0E1eM3jpRP2izCuB3OorQHFiOANkA7NiKYoszRlKON/QrWqqFSwXA1WWmjIOTxWyJBFwOzBU92n1zvQXr32Lt34Y6JyU6mipK3ty76Tzk/Si922eyrJWqt8okiQR6fLjmFncce368PzSGByKXBbtEdhz1hpo6bzGbw5O8J8vDLDm+F28S/kFfdI4scA6pi/9AoWNt9QkRwoEgvkjSxK71raxa+1NJHPX88CBCb74wmHUwce4pLCXq449z/pjDwBmqFehYzv57gspdF9IvvuCihosAgHgpTbZqfS5esTmA9YBfwvcBbwK+D+RSOSFaDT6WPWOFUWipeUUz+i21tbKmotpzcAbN09ZkZahzTYoilzZjiba5M/peJM5/AH3ijiH5aSm/+YiLyH5XKA48Z5GfdfSAt0d/qaMF28x7LSmX1rWgVeC0OryOG3e/XeKqHsOzaA7kUZdGD3nLfga67qB1xdjR19LRRskn/mq9LR4V2zfne2cVQZaMlfg8SPT3PfyKBOHnuR13Mfn1EfxqykSnRcyc+Hfk1t73akPCRAIzhJ8TpWbtnVx07YuEtkLeeTwJJ8+OMHLA0fYkHuZC+R9XDp+kO1j38S756sAaJ4OtI7tFNq3U2g/h0LHdrPwqXhOz1Yy1Bpipc/V8Yn/A3BFo9G/Kn5+JhKJbAc+AtRI12mawfSpDHFcIIl4hlQyR9Cjcn5vaEW0uaXFO+92+CSDbDpPSGFFnMNyMu/+K2RwJbMYKuROw77LzL0KqaKYhW2/SJ1gqUO1kPvvVNDwHJqh99Xm/ydxblf0t9S0wZU0+y47nVqxfXe60NGxNHnLZ7SBpukGB8eT/PboNA8fnmT8+D5eJT3OnzoeZYs6gCa7yG28halzf5dC9wXL3VyB4KzC71K5fksn12/pRDe2sH/scp48Os1nj00THZqiK3uQC+T9nKsNsDMzwLqjv8FbjBYvqF6yrVuhYytG20YKbZvRWjei+3pOXZKDYLkYBFojkYgzGo2WRMi6Mb1o1UVpLgKer1r2FPCHS9vEpaU35Can6axp9dYUHz6d8DgUrosI0SCBPRf2t8y7LppAcKZwRhloeU3npZEEzxyf4dnBGZ4fnGBDbh+Xy8/zCdczbHIeAiDXsYP41r8ju/k224KeAoHg1CJLEpFOP5FOP2+7sA/DMDgRv4gXT8R5fiTBz6bSDE3M4J7Zz2YG2F4YYNvIETaP3kWLNCuHnJG9THrWkvCvJx9cA6G1qOF1uDs24AmEkRoonglOG54Fcpgy+/cXl10OPFUlEAIwBFQXJdoKHFzKBi41kiTNO+dDcIaykJoLpwnhU6SsuZRctKaFWGZx6wkuBrm+y5HyieVuhqABp6WBZhgGU+k8+8eS7B9LcmAswb6xJLGJIbZyiHOlw/yR6zDnyy/idqUxkCh0voLE+o+S3XCTWSxTIBCsWEpKZz1BN9dunp1h1/SLGY5lODKZ5pl4hnviWTIzJ3DNHCKUOkhH9ihrYsfYmHiIrpGfVuwzZngZlLoZVXuIOzpIu7vIebvR/d14O9egOdsJeL2EPCoht4OQxyFmb1cg0Wg0FYlEvg78UyQSeSem9+zPgPcCRCKRbmAmGo2mgS8Dj0QikT/HVHHcDbwLqF8NWCA4HVBcGK4ghfkUURacctq8zkVRhFxsDG87BkKkYyWz4gy0gqYzkykwnc4zk8kznS4wnsgynilweDTB0HQKLTZER2GYNdIIa6QRbncMc650mLBzfHY/wQ3ke9/AzOrLyfdeirHIhRoFAsGpR5El+lo89LVYVcvWAZeUP2XyGidSeaLxGPmJwzB9GGXmKJ7kUQLpQTbnBmhNP4k7nYWp4kZR0A2JCYIMG22cMFp40QgQk4Ok1FZyzhbyrjZ0dxt4w6i+MB5/iKDHRcjjoMWtEvI4CLkd+F3KvGtICebNnwJfBH4NxICPR6PR7xa/G8Y0wr4WjUafiEQirwX+Bvgr4DDwtmg0+utlaLNAsHhIErk11yx3KwQCwRIhGQ3c42Nj8cX3nRsGAxNx/u6XL5HJZpG0LE49hVNL4tZSqFoSP2n8UppW4nRIM3RKU3TJM/TIM3QYkzjLtc/BkB1owX4KHedQ6NxJoeNcCh3nnNJio8uJSO60R/SLPaJfihgGUi6GnBhGnxlCzo6SGBmA+AnU5BCOzASu/DSe/BQOI1d3NxnDQRI3KcNNChcp3GRwgqwiywqyoqIoCoqiFv8pqKqKqijFYp6gSqDKpvGpyhKyVGwfpdevUQxjMirCmQod55K66L8vZS8B879nOjoCp611ms9rhng+FoZ4t5wcov9ODtF/C0f03cmxVL95S+5Bcww+QvAXf4BUyIBeQDI0OoC77VZWiv8sFBwBNE8HjpZV5FxbKPi6yYbWooXWooXWoPtXzdbEEAgEgmaQJAxXCM0VgvAWAi1etKofqCyQNQwopJHTE8iZSeT0BFJmElKT5NNx8uk4hWwSPZvAlUvgzqegkMbQCuh6FkMvQE7H0DUwNCRDR0FHRkdHIotEBjDNMancNtMDJ5l/Q+Xn4rJDYwrfPPESsrkYSZKKf0vImHl9Uum74r4NZg286rm5G7d2csHqlqXpb4FAIBAIBE3T0IMmEAgEAoFAIBAIBIJTh8iAFwgEAoFAIPj/7Z17sFVVHcc/VIKDqZVPRDNf/RQdHxmFiIbZZCm+8IHJZJTiEyfQ0MxXMQoMqGkSiiYDTuZrtCAa09REUUkBFUv75iMf8dLBR4GgqPTHbx3cHDj3nsPl7H3uub/PzBnuXmvv4be/s9ZvrbXXWr8VBEHQIMQALQiCIAiCIAiCoEGIAVoQBEEQBEEQBEGD0DDRNcysC3AtcBy+P/8qSWMq3HsAcDWwK/ACcJ6k+/KyNU9q1OWbwBXAl/HDXIdJejIvW4sg6TMbGCrp/gr3bA/cCOwPvAacI+me/KzMn2p0ydy7M/AssPFaDvptKqosLwcDo/EDjecBYyTdlJ+VxVClNv2AUcDOgIALmqEu1eJnOwprKw9m9gVgAnAI8BZwqaTJmWf2Aq4H9gKeB07PtkFmdjwwEtgG+AswWNIb+bxRPpjZTnj/pA+wFLgduFDS8tbaIjM7CLgGr19PAKdIejGTfzZwPrApfq7fEElLc3mxnDCzXYFxQC9gMTBO0tiUF/pViZn9BthZUt90vc5108w6AZcBg4ENgJuA8yV9lNsL5YCZnQjcUpY8RdJRRZS9RppBGwv0Br4FnAZcZGYnlN9kZlsCfwTuBPYE7gD+kMRrRqrVZTfgz3jF2heYCjxgZtvmaGuumNmGwK3A7i3c0wmYgjv6nsBk4C4z2yEXIwugGl0y924HTAM2rLddRVNledkF1+P3wN7ACODXZnZ4LkYWRJXa9MAblgnpvt/ivnfHXIysL1X52Y5CC+VhErAZ3kkZAUwws97pmY2Ae4CZeBv0CPAnM9s45Zf872V453sT4OZ6v0uemFlnvH/yPl6eBgJHAZe31hYlXzwVr1dfBRYCU8zsUym/P67dmfiB6z2BK3N7uRwwsw3wMvQa7n/PAi42s4GhX/Wkj4wnZ67bWjeHAYPwD1hHA98Dhtf7PQqgB972d8v8BhVV9hpigJYKz2D8S91sSVOAMcCQtdy+P4Ck0ZJekjQSWIYXqqaiRl3OBGZLOl/OGOAx3ME1HamzOBPYqZVbDwIMOFXSc5JG47qc3PJj7ZMadMHMjsK/kL9fb7uKpgZdBgBPSxop6UVJt+AN1cB621gUNWizHXCtpHGSXpZ0BT5D0K59b41+tumpVB7SzNDhuC99VtJEvENyZrplALACOFfS83in7t2UDnA2cJekSZLmAicBh6QZ/Gbha/gX9EGSnpc0HbgY9x+ttUWDgWckjZH0HPAjvM6VTqMeite/qZJmAafjncfP5vVyOdAdn304K/nfacD9wDcI/aoi+bMbgEczyW2tm0Px2fLpkh7CZ4KasW/ZA5graWHm9w4Flb2GGKDhU65dgBmZtBlATzMrX4a5GNjUzI4zs06pk7kxMDcfU3OlFl12BB4vS3sG2K9+5hXKAcB9tP5+vYCnJP0vkzajiufaK9XqAr5M6ULgx3W1qDGoVpc7WLNjvpLmnmGsShtJ90oaDv6l28xOxv1Tud9pb9TiZzsClcrD14EF2WU7rO5LewGPSvoYQNJKvJOYzX+49KCk14FX1/L/tGcEHCppSSZtJV6+WmuLyvV5D5gD7Gdmn8a/uj+ceXYmvk1ln/X9EkUh6RVJAyQtS/27/YEDgQcI/arlcuCh9CuxznXTzLbBBxtZ7WYA26aZo2aiB16Hyymk7DVK49MNeEvS8kzaIqAzsAWwIJP+CL4++XbgY/xo61PSV4FmoxZdFgHlyxm3Bzavq4UFIWlC6W8za+nWbsD8srS1adUU1KALks5I9/Wtr1XFU60ukv6VvTazrYATgF/UzbiCqaXMpHt2Bf6O+96fSvp3/azLhVr8bNPTQnlozZd2Y83OzSJ8qVo1z7d7JL2Jz/gAkJY4DcH7LdXoVyn/c/hHolX5kj40s8U0kX5l/AffDzUNX1p9NaFfi5jZfvgyxD2AczNZbamb3dL1/LI8Uv7rbbO6MUjLk3cC+pnZCHwC607gUgqqu40yg9aVNZdZla67lKVvBOyAr+fsiU+1XmNm7XqZTQVq0eU2oL+ZHWNmnzGzw4Aj8E5GR6aShuX6BcFqpKUid+OO9fqCzWkkFuK+92xghJkdU7A9baUWP9uRqaRT57RHozVf2xF98VX4V/ILaJs+XTPXlZ5vNo5Mv68AvyT0a5EU1OcmfKn222XZ61u7ZvSPu+CTVkuBY/E9dgPxOlxI2WuUGbTlrGlo6fq9svThQBdJl6Trp8xsd+AioF/9TCyEqnWRdJ+Z/QzfE9AZn14dxydrYDsqy/GoOVm6sGa5CoJVmNmm+JfbHYE+aclCAKQ1+U/hvncP0v6FYq1qE7W0Px2ZSjotk7TSzCrllzRsLb9pSAPWq/H9ecdK+kfSp6W2qJI+i1MeFfKbTj+AtFcHM+uKB2WYSOjXEpcAL0i6cy15bambWe1WZP6G5tGOVEc3l7Q4JT2T6vGtePTG3Mteo8ygzQM+n6YYS2yNjzDfKru3J768JstsvCPVbNSiCykwyCZAd0k9U/Ir9TaywZmHa5ZlazrYsqWgesxsc+CvuE/pK+mlgk1qCMxsz1LEvgzP0f6XUdfkZzswrfnStuY3BWlZ40TgDGBACjoDbdOn1NFblZ/2R25GE+lnZt3N7Iiy5Ofwj84LCP1a4kQ8sMcSM1sCnAcckP5uS9mbl7mm7O9m0Q6AzOCsxPP4sQLzKaDsNcoA7WngAzwsbYk+eFTC8nOZ5uPh9bPsBjRjJ6pqXczsBDMbJ2mFpIVp5N8P72h2ZGYCe6flaiX6pPQgWI3USZ+GDzoOlLS2DcMdleOB68rS9sUbsfZMLe1PR2Ym0N3MvpRJy/rSmUDv1PaUZpF6l+X3KT2YAgx8kebzxVfineX+ku7OpLfWFpXr0xVfHjkzBXd4MpuPByj4EJ/NbhZ2A+42P06pxL7Am3hQhtCvMn3xvWd7p9+NwKz09zrXTUnz8WMPstr1AeanYCJNgZn1N7NFZR/q9gHeoaC62xBLHCW9Z2aTgfFmNggfaf4EOBXAzLYG3pW0DA8f+piZnYdvHD0I+CFwaBG215MadfknMNnMZuCF4QJ8v96kAkwvFDPbAl92swSYjkcjmmRmP8cHrb1o0jD7LVGmS5Ao02UY3iH4DrA01TGADyR1uNmUMm0mAsPM7HLcr3wXD6DSrqPwteZnA0fSy2Z2L3CzmQ3B60kpfDx4ezwauNbMxuOhpzfB90eDD+6nm9mjeIfmGuAeSS/k+Bp1Je2FH4q3v7My/gNab4smAsPN7EL8LKaL8Y7xAyl/PHCjmc1N6eOBiU3mz6fjM2aTzOxcPGjDaDwyYejXApJezV6b2du4737RzN6gbXXzOmCUmb0GfASMSvc0E9OBTsANZjYS35M2Nv0eooCy1ygzaADn4AOLB/EN+SMk3Z7yFpDOa5D0BB78YgAeWn8oMFDSg7lbnA/V6vI0XulG4uH1uwMHq5WTypuUJ/EOFvKT7o8EtsSXwp4EHC3plcKsK45VugSrkdXlOPzD1f14/Sr9phZjWuFk69LL+MD127jvPQ3fXzOnOPPWGy352eATTsK/KP8N3/NyiqTHAST9FzgM/zI/Bz+z9NBSaOp032B8v/jj+DlMP8j7BerMsenfUazuPxbgnb+KbVH6tz/wfXzmYyvgyExo9Nvw4GjX4f5pFqtH6mv3SFqBd34/xMvYBHwv369aa8tDv8qsh7o5Fvgdvtf4Lnxf1hV52Z8HaXnjIXj08zn4ZND1wKiiyl6nlStXrrcXDIIgCIIgCIIgCNadRppBC4IgCIIgCIIg6NDEAC0IgiAIgiAIgqBBiAFaEARBEARBEARBgxADtCAIgiAIgiAIggYhBmhBEARBEARBEAQNQgzQgiAIgiAIgiAIGoQYoAVBEARBEARBEDQIMUALgiAIgiAIgiBoEGKAFgRBEARBEARB0CD8H/g1zEXmTjOBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x576 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pm.traceplot(trace);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The left column consists of a smoothed histogram (using kernel density estimation) of the marginal posteriors of each stochastic random variable while the right column contains the samples of the Markov chain plotted in sequential order. The `beta` variable, being vector-valued, produces two histograms and two sample traces, corresponding to both predictor coefficients.\n",
    "\n",
    "In addition, the `summary` function provides a text-based output of common posterior statistics:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/twiecki/anaconda3/lib/python3.6/site-packages/mkl_fft/_numpy_fft.py:1044: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  output = mkl_fft.rfftn_numpy(a, s, axes)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>mc_error</th>\n",
       "      <th>hpd_2.5</th>\n",
       "      <th>hpd_97.5</th>\n",
       "      <th>n_eff</th>\n",
       "      <th>Rhat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alpha</th>\n",
       "      <td>0.91</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.72</td>\n",
       "      <td>1.11</td>\n",
       "      <td>9283.21</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta__0</th>\n",
       "      <td>0.95</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.77</td>\n",
       "      <td>1.12</td>\n",
       "      <td>10340.59</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta__1</th>\n",
       "      <td>2.64</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.67</td>\n",
       "      <td>3.71</td>\n",
       "      <td>7468.93</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sigma</th>\n",
       "      <td>0.99</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1.12</td>\n",
       "      <td>8343.05</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         mean    sd  mc_error  hpd_2.5  hpd_97.5     n_eff  Rhat\n",
       "alpha    0.91  0.10      0.00     0.72      1.11   9283.21   1.0\n",
       "beta__0  0.95  0.09      0.00     0.77      1.12  10340.59   1.0\n",
       "beta__1  2.64  0.51      0.01     1.67      3.71   7468.93   1.0\n",
       "sigma    0.99  0.07      0.00     0.85      1.12   8343.05   1.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pm.summary(trace).round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Case study 1: Stochastic volatility\n",
    "\n",
    "We present a case study of stochastic volatility, time varying stock market volatility, to illustrate PyMC3's use in addressing a more realistic problem. The distribution of market returns is highly non-normal, which makes sampling the volatilities significantly more difficult. This example has 400+ parameters so using common sampling algorithms like Metropolis-Hastings would get bogged down, generating highly autocorrelated samples. Instead, we use NUTS, which is dramatically more efficient.\n",
    "\n",
    "### The Model\n",
    "\n",
    "Asset prices have time-varying volatility (variance of day over day `returns`). In some periods, returns are highly variable, while in others they are very stable. Stochastic volatility models address this with a latent volatility variable, which changes over time. The following model is similar to the one described in the NUTS paper (Hoffman 2014, p. 21).\n",
    "\n",
    "$$\n",
    "\\begin{aligned} \n",
    "  \\nu &\\sim exp(0.1) \\\\\n",
    "  \\sigma &\\sim exp(50) \\\\\n",
    "  s_i &\\sim \\mathcal{N}(s_{i-1}, \\sigma^2) \\\\\n",
    "  log(r_i) &\\sim t(\\nu, 0, exp(-2 s_i))\n",
    "\\end{aligned}\n",
    "$$\n",
    "\n",
    "Here, $r$ is the daily return series which is modeled with a Student-t distribution with an unknown degrees of freedom parameter, and a scale parameter determined by a latent process $s$. The individual $s_i$ are the individual daily log volatilities in the latent log volatility process. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Data\n",
    "\n",
    "Our data consist of 401 daily returns of the S&P 500 stock market index during the 2008 financial crisis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "401"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "returns = pd.read_csv(pm.get_data('SP500.csv'), parse_dates=True, index_col=0)\n",
    "\n",
    "len(returns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "returns.plot(figsize=(10, 6))\n",
    "plt.ylabel('daily returns in %');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Specification\n",
    "\n",
    "As with the linear regression example, specifying the model in PyMC3 mirrors its statistical specification. This model employs several new distributions: the `Exponential` distribution for the $\\nu$ and $\\sigma$ priors, the Student-T (`StudentT`) distribution for distribution of returns, and the `GaussianRandomWalk` for the prior for the latent volatilities.   \n",
    "\n",
    "In PyMC3, variables with purely positive priors like `Exponential` are transformed with a log transform. This makes sampling more robust. Behind the scenes, a variable in the unconstrained space (named \"variableName_log\") is added to the model for sampling. In this model this happens behind the scenes for both the degrees of freedom, `nu`, and the scale parameter for the volatility process, `sigma`, since they both have exponential priors. Variables with priors that constrain them on two sides, like `Beta` or `Uniform`, are also transformed to be unconstrained but with a log odds transform. \n",
    "\n",
    "Although, unlike model specification in PyMC2, we do not typically provide starting points for variables at the model specification stage, we can also provide an initial value for any distribution (called a \"test value\") using the `testval` argument. This overrides the default test value for the distribution (usually the mean, median or mode of the distribution), and is most often useful if some values are illegal and we want to ensure we select a legal one. The test values for the distributions are also used as a starting point for sampling and optimization by default, though this is easily overriden. \n",
    "\n",
    "The vector of latent volatilities `s` is given a prior distribution by `GaussianRandomWalk`. As its name suggests GaussianRandomWalk is a vector valued distribution where the values of the vector form a random normal walk of length n, as specified by the `shape` argument. The scale of the innovations of the random walk, `sigma`, is specified in terms of the standard deviation of the normally distributed innovations and can be a scalar or vector. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "with pm.Model() as sp500_model:\n",
    "    nu = pm.Exponential('nu', 1/10., testval=5.)\n",
    "    sigma = pm.Exponential('sigma', 1/0.02, testval=.1)\n",
    "\n",
    "    s = pm.GaussianRandomWalk('s', sigma=sigma, shape=len(returns))\n",
    "    volatility_process = pm.Deterministic('volatility_process', pm.math.exp(-2*s)**0.5)\n",
    "\n",
    "    r = pm.StudentT('r', nu=nu, sigma=volatility_process, observed=returns['change'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that we transform the log volatility process `s` into the volatility process by `exp(-2*s)`. Here, `exp` is a Theano function, rather than the corresponding function in NumPy; Theano provides a large subset of the mathematical functions that NumPy does.\n",
    "\n",
    "Also note that we have declared the `Model` name `sp500_model` in the first occurrence of the context manager, rather than splitting it into two lines, as we did for the first example."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (2 chains in 2 jobs)\n",
      "NUTS: [s, sigma, nu]\n",
      "Sampling 2 chains: 100%|██████████| 5000/5000 [02:04<00:00, 40.14draws/s]\n",
      "/Users/twiecki/anaconda3/lib/python3.6/site-packages/mkl_fft/_numpy_fft.py:1044: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  output = mkl_fft.rfftn_numpy(a, s, axes)\n",
      "The estimated number of effective samples is smaller than 200 for some parameters.\n"
     ]
    }
   ],
   "source": [
    "with sp500_model:\n",
    "    trace = pm.sample(2000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can check our samples by looking at the traceplot for `nu` and `sigma`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/twiecki/working/projects/pymc/pymc3/plots/__init__.py:40: UserWarning: Keyword argument `varnames` renamed to `var_names`, and will be removed in pymc3 3.8\n",
      "  warnings.warn('Keyword argument `{old}` renamed to `{new}`, and will be removed in pymc3 3.8'.format(old=old, new=new))\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pm.traceplot(trace, varnames=['nu', 'sigma']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally we plot the distribution of volatility paths by plotting many of our sampled volatility paths on the same graph. Each is rendered partially transparent (via the `alpha` argument in Matplotlib's `plot` function) so the regions where many paths overlap are shaded more darkly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 8))\n",
    "returns.plot(ax=ax)\n",
    "ax.plot(returns.index, 1/np.exp(trace['s',::5].T), 'C3', alpha=.03);\n",
    "ax.set(title='volatility_process', xlabel='time', ylabel='volatility');\n",
    "ax.legend(['S&P500', 'stochastic volatility process']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, the model correctly infers the increase in volatility during the 2008 financial crash. Moreover, note that this model is quite complex because of its high dimensionality and dependency-structure in the random walk distribution. NUTS as implemented in PyMC3, however, correctly infers the posterior distribution with ease."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Case study 2: Coal mining disasters\n",
    "\n",
    "Consider the following time series of recorded coal mining disasters in the UK from 1851 to 1962 (Jarrett, 1979). The number of disasters is thought to have been affected by changes in safety regulations during this period. Unfortunately, we also have pair of years with missing data, identified as missing by a `nan` in the pandas `Series`. These missing values will be automatically imputed by `PyMC3`. \n",
    "\n",
    "Next we will build a model for this series and attempt to estimate when the change occurred. At the same time, we will see how to handle missing data, use multiple samplers and sample from discrete random variables. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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t9nH2KcfHHkMva3cNy8YJUszvMsbsBrYaY24GZv1OsNY+aa19pbX2hdba51pr7+g40g5MT02wNTe0bnHWno2fnppYc+ypJ4y0XMjNzgvSX5C+g8bpp1Vbp54wEqitsOPWjdjDttVuHzdeeEbsMfSydtewbBzfSxMBjDEvBZ4B/MBa+/lOO+32pYlw9JrZPXP7yS+tkhvOsn1yS9PrZLPDA3zgnx9kz9x+Hl9aZaj6SH2lWG55nld/YdoIE2e7Ob/h/NNZXSp01MZGxe7XVpg/Z/36iDKfKHR7G6LdNdwubbP4a3Vpomcxr77wJ437fuaX4m4jpoEvBL2ixctGFPMwkryoINn5Kbf+leT8ulHMWz0BegVwHXAy7guGwH1O6K6OIxIRkUh5FnNr7a3ArcaYK6y1t3UxJhERCSnIpYnfNcY8F/fywhuBG621X4s3LBERCSPI1Sw3Ayu4Lxz6r8A7Y41IRERCC1LMV4H/CwxYa+8h2KN5ERHpoiDFvALsBr5ojHklwV40JCIiXRTkUfaluB/k/CVgW/VnERHpIYFezg+MAtPAqegDnUVEek6QR+YzwIPAWcAS7htniYhIDwn0gc7W2tcDPwBeDDwl1ohERCS0QMXcGDMEHIf7ZOimWCMSEZHQghTzm3A/Nu6rwDzuI3QREekhvnvm1to9te+NMZ+x1j4Rb0giIhJWkI+NuwT33RIHgb8xxrzHWvve2CMTEZHAgmyz/DnwT8Cf4F6a+PJYIxIRkdCCFPPax70dqn58nPeH0ImIyIYIUsx/ivshzrcZY94JfDfekEREJCzfYm6tfTXwDGvtncDN1tqrYo9KRERC8Szmxpjrq//+I/Ch6oc6/231XxER6SGtrmapfXDzzd0IRERE2tfqY+PmjDFnAb8HnAjsAz5jrf1ht4ITEZFgWm2zXALchvuqzy8Dh4AZY8zFXYpNREQCarXN8kbgPGvtkQ+jMMZ8FPgccEfMcYmISAitinmxvpADWGsPGWNKrRo0xmRxH9H/Cu6rRt9trf1cp4H2qsVCiV2z88zMLXBwqcjocIYdk+NMT00wMpDe6PCaenKlyC13P7Qm5ovPPBkchzvu309+qchgxsHBYaVYXpdTmJz7cXyaqc/Db3y6HU+U49puu1GeV78W+3nNdJtTqVSa3mCM+bq19neD/r7u9p3ApLX2TcaYpwJ7rbWn1h9z4MCh5p32iFxuhHze/23bFwsldu7ey778MoVS+cjvB9IptuaGuP3yc3pu8S0WSlz5qTl+9ujimpgBHNy3xWymltNNO87i6pn7AuW8EeMTdO7C8MqjXjfmvJZbXOPabrtRnwfr12Iv36eCiGpdjo1tdrxua3Wd+W8ZY3Y3fP0jcIZPf58B3l73czFErH1l1+x804VYKJXZl19m1+z8BkXmbdfsPA8/tr6Qg3chh6M5XXfnA4Fz7sfxacYrj3rdzCmucW233ajPg/Vrsd/WzEZoVcxfCdzS8HUzPp8Baq09XN2O2Yz7KUXXRxRrz5mZW/C8gxdKZfbM7e9yRP5m5hZYKXoXpVYKpTL3PvJE4Jz7cXyaaZVHvW7lFNe4tttuHOeFbUtaX5r4zXYbNcZMAJ8FPmitXfcio02bBslkevdPpXQ6RS434nvcwaXWf3Tkl1cDtdNNfjH78dsfq895I8Yn6NyFEWbM4pzzWm5xjWu77cZ1Xpi2el0c67JRkM8ADcUYcxLuB1m8wVr7tWbHHD68EnW3kQq6vzU6nCHfYkHmhrKR7992yi9mP6321WFtzhsxPnHsmYcZszjnvJZbXOPabrtxnRemrV4X4Z65522BPjYupOtwPyf07caYb1S/hmPoZ8PtmBxnIN18CAfSKbZPbulyRP52TI4zmGlv2gfSKc4+5fjAOffj+DTTKo963coprnFtt904zgvblsRQzK21b7TWnmyt3Vb3tRR1P71gemqCrbmhdQuy9sz79NTEBkXmbXpqglNPGGl6J/J8mpyjOd144RmBc+7H8WnGK4963cwprnFtt92oz4P1a7Hf1sxGSN9www1d73RxsdD9TkMYGsqyvLzqe1w2neKCM07CAX766CIrxTJPGc5y2TNP4R0vNT15CVU2neKPzj2NlZXVNTFfes44k1tHefixJZaLZYYyKTIph1K5sian0eFs4Jw3YnyCzl0YjXm0Gp8457yWW1zj2m67kZ1XWr8W++E+FURU6/K44wb/wus2z+vM45SU68z7VZLzU279K8n5bfR15iIi0idUzEVEEkDFXEQkAVTMRUQSQMVcRCQBVMxFRBJAxVxEJAFUzEVEEkDFXEQkAVTMRUQSQMVcRCQBVMxFRBJAxVxEJAFUzEVEEkDFXEQkAVTMRUQSQMVcRCQBVMxFRBJAxVxEJAFUzEVEEkDFXEQkAVTMRUQSILPRAcRhsVBi1+w8M3MLHFwqMjqcYcfkONNTE4wMpDc6vECC5NALeYaJof7Y/FKRwYyDg8NyscxgxoEKrJQqOEAFGB3KcMnZ/m3V93vFC5/ObXc/tK6PlWI5cGztjKVfbvUxXHzmyeA43HH//jV9bZ8cZ8/cQugYopgDv/EJ01ezPhrndjDt4Dhr+33D+af7jnMn8xXF/cWvjSDrMq77qlOpVCJpqJEx5jnAX1trtzXeduDAoXg6xR3snbv3si+/TKFUPvL7gXSKrbkhbr/8HN/By+VGyOcX4wrRV5AcgLbzjCq/MGPtdayfgbTD1txwoLayKQfHcb8vlJovsTCxBV0z7eRWK2r1seP+tyb2+hjGn7Z53bxFPQetcu5kXfoZSKc47akjfPjSSd/7ZzvzFUVd8Gvjph1ncfXMfYHXZZi+a8bGNjtet6VvuOGGQI2EYYy5Fng7ULnmmms+3Hj74mIh+k6rbrvnYe76yWPrFlKpUuHJQgkHeNapuZZtDA1lWV5ejStEX0FyuG/hibbzjCq/MGPtdayfUoXAbZUr7vEedTx0bEHXTLu5NcZebhJ7fQzPP31s3bxFPQetcu5kXfopVSo8uVKEJv02ame+oqgLfm3cv3CQH/9yMfC6DNN3zXHHDf6F121x7Zn/GPjDmNpuaWZuwXMhFUpl9szt73JE4QXJoRfyDBNDq2P9RNlW2PaCjGWn8fhpFUMcc+DVX6fr0s9KMdi6bWe+ori/+LVx7yNPhM49yvtqLHvm1to9xphf8bp906ZBMpl49nQPLhVb3p5fXiWXG2l5TDqd8j0mTkFywGejqlWeUeUXZqz9jvUTZVth2/NbM1HE4ye/vNp03uKag2Y5R7Eu2+m3UTvzFUVd8Guj3dSD9B3EhjwBevjwSmxtjw5nyLcY9NxQ1ne/eKP3zIPkUKHSdp5R5RdmrP2O9RNlW2Hb81szUcTjJzeUpVQqr4sjrjlolnMU67Kdfhu1M19R1AW/NhqfBwkqSN81Y2ObPW9L3KWJOybHGUg3T2sgnWL75JYuRxRekBx6Ic8wMbQ61k+UbYVtL8hYdhqPn1YxxDEHXv11ui79DGaCrdt25iuK+4tfG2efcnzo3KO8ryaumE9PTbA1N7RuUGvPHE9PTWxQZMEFyaEX8gwTg9exftyrWYK1lU05DKTdL+/2gscWdCzbya0xwmzKIdskdr8Yop6DVv11si79DKRTnHrCSKB12858RXF/8WvjxgvPCLUuo76vxnI1C8A111yTb3YlC8R7NUs2neKCM07CAX766CIrxTJPGc5y2TNP4R0vNYEuAdroq1mC5NBJnlHlFyaGxmOXi2WGMikyKYdiucJQJkXacZ/xry353HCGy5651betI/3+9in8/WXnUC6W1/VRKlcCxxZ2zQTJrT6GS88ZZ3LrKA8/trQm9nf9wW8ykE55xtBs3qKag1bjE6Yvrz4a53Yw45BNpdb0+95LJkkHuFS6nfmKoi74tTE6nA20Ltvpu6bV1SyxXWfeSpzXmUdho/fM45bk/JRb/0pyflHl1uo688Rts4iIHItUzEVEEkDFXEQkAVTMRUQSQMVcRCQBVMxFRBJAxVxEJAFUzEVEEkDFXEQkAVTMRUQSQMVcRCQBVMxFRBJAxVxEJAFUzEVEEkDFXEQkAVTMRUQSQMVcRCQBVMxFRBJAxVxEJAFUzEVEEkDFXEQkAVTMRUQSQMVcRCQBMhsdQFQWCyV2zc4zM7fAwaUio8MZdkyOMz01wchAOtR5f/Kc07jkGSeHPi9IfxJe/Vjnl4oMZhwcHFaKZUaHM1x85sngONxx//6mc+F3vtexQeY1zPHNjvWKffvkOHvmFvjMvY9wcLmEA1SAwbSD4zSPvb6PMOf18lpud+7qj10ulluuGa9ja99TgZVS5chYjg5luOTso3PU7rqMmlOpVCJv1BiTAj4ITAIrwJXW2h/Vbj9w4FCknS4WSuzcvZd9+WUKpfKR3w+kU2zNDXH75ed43hGbnTeYSXHKaPjz/PrrFbncCPn84kaHEYjXWDeq3dFqanNx046zuHrmvpbn+x3rNa9h1kGrPBpjz6ac6i8rrHqnvK4vgJ279zL/+BKr5dZ3sXZzjlPjugwy92HmuVHjuIeRTQGOgwMUSt6teK3Ldsd1bGyz43VbXNssrwCGrLXPBd4KvC+mfgDYNTvfdBILpTL78svsmp0Pdd5Ksb3z/PqT8LzGulHj3ak2F9fd+YDv+X7Hes1rmHXQKo/G2FfLFVZL/oW8sa9aH36FvP68sDl3U5C5DzPPjTp5RLlahtVSpWUhb9ZHnOMaVzF/PvBlAGvtPcCzYuoHgJm5Bc9JLJTK7Jnb3xPnSXitxtpPoVTm3keeCHS+37HN5jXMOugkDz+1vsL20U7O3RQ0nzDz3AviGte49syPBw7W/VwyxmSstUWATZsGyWSi+9Pt4FKx5e355VVyuZENP69XpNOpno6vnt9Y+wnz6Mvv2MZ5DbMOOs3DT355ta2HmmFzjlPjugwzZtFvFscrjnGNq5g/AWyu+zlVK+QAhw+vRNrZ6HCGfIuJzw1lm+4Rd/u8XtFPe+Z+Y+0nzL6o37GN8xpmHXSah5/cUJYKldB9hM05To3rMsyYdbL/vRHaHdexsc2et8W1zXI38AcAxphzgftj6geAHZPjDKSbpzKQTrF9cktPnCfhtRprPwPpFGefcnyg8/2ObTavYdZBJ3n4qfUVto92cu6moPmEmedeENe4xpX9Z4FlY8x3gL8F3hxTPwBMT02wNTe0bjJrzxxPT02EOm8w0955fv1JeF5j3ajxKf7aXNx44Rm+5/sd6zWvYdZBqzwaY8+mHLJpx71iwkd9X7U+sinPCx7WnRc2524KMvdh5rmR/yh5y6Ygm3YYSLduxWtdxjGusVya6CfqSxPh6PWoe+b2k19aJTecZfvklsDXmdef98fPOTXwdeZh++sF/bTNAmvH+vGlVYYy7h12pVgmN5zlojNPAsfhc/f/3N2LHFo7F37nex0bZF7DHN/s2DWx151/5BrmexfILxePXi9edy1zs76OXGsd4rxeWcvN1mW7c1d/7HKx3HLNeB1b+75Sqay5zjzX8FqAdtdlO1pdmpiYYh6lfit2YSU5P+XWv5KcX1S5bcR15iIi0kUq5iIiCaBiLiKSACrmIiIJsCFPgIqISLT0yFxEJAFUzEVEEkDFXEQkARLzSUNBGWOeA/y1tXabMeZs4GagCDyI+yEaZWPMy4B3Vk/5P8DVwBDwCeBpwCHgVdbaA11PwEfA/P4cuAwoAzdaaz9rjBmmx/NryO2ZuLmtAPcCb6zm9k7gAtyc32St/Z4x5teAj+K+gO/fgKuttT31fqkBc3sP7ttLZ4APWWtvNcacCOwGhoEFYKe1tudeeRMkv+pxI8B3gLdaa7/cD/kFnLtXA1cBaeAOa+27os7tmHpkboy5FvgwbmEGt2D/pbX2+cAgcIExZjPwHuBCa+25wEPAibgTcb+19gXAx4Hruxy+r4D55YD/BDwX+H3g76rH9nR+TXL7EG6xfgHu2y1fXr0jnQc8B/gj4Kbqsf8NuL56rANc3M3Y/QTM7UXAr1U/8OX5wFuMMU8B3gHsrh67F3hd1xPwESS/usNvYu0bIPZ0fgHn7j/g3r+2Ac8GBowxWSLO7Zgq5sCPgT+s+3kvcIIxxsF9y95V4Hm47/L4PmPMXcC/Vx+hHvnADeCFUBTtAAAEE0lEQVRLwPldizq4IPk9CfwMOK76VXuE2uv5Nea21Vr7ner3d+PG/3zgq9bairX2YSBjjBkDfhv4ZvXYfs3tX4Arqr+r4D7CW6X35w2C5Uf1L8bvAHN1x/Z6fkFyOx/4PvAx3HV4t7U28rk7poq5tXYP7h2g5ofA+4H/B5wEfAP3UfiLgLcALwPeZIw5nbUfuHEIGO1O1MEFzA9gHngAdwvp/dXf9XR+TXL7iTHmvOr3L8f9H1Pjh6LU8nCstZWG3/WMILlZa5ettY9XH9F9DHeb5TA9Pm8QLD9jzO8Bv26tvbXh9J7OL+C6PBF4IfAaYDvwD9W/kCPN7Zgq5k38PfACa+1v4G4tvA94FJi11v68emf5FnA2az9wYzOQ34B4w2qW38uALcCvAqcCrzDGPJv+y28n8DZjzBeAXwC/ZP2HotTyKDf5XS9rlhvVbZUvAw9Ya/+qemy/zRs0z+81wJnGmG8ALwX+pvqcT7/l1yy3R4FvWGsPWWt/gftA6nQizu1YL+aP4Q4ouE9APAX4V9xFdaIxJgOcizv4Rz5wA7cg3tXlWNvRLL/HgSVgxVq7jLuAcvRffhcAV1hrLwCeCvwTbg4vMcakjDGn4n7C1S+BvcaYbdXz+jK36hPUXwNus9a+q+7Yfps3aJKftfZya+3vWGu34f4P61pr7b30X35e63KbMWbIGHMccAbwIyLO7Zi7mqXBlcAnjTFFoAC81lp7wBjzNuAr1WM+ba39N2PMT4CPGWO+XT328uZN9pRm+T1kjDkfuMcYUwa+jbvgvk1/5fdD4IvGmEXgf1trvwhQfZ7jX3AfqFxdPfa/ALcaYwZwt5xmNiDeMNblZox5M/B04LXGmNdWj9sJvBt33l6L+yiw1+cNPObOQ7/l57UuP4JbvB3gXdbax4wxkeaml/OLiCTAsb7NIiKSCCrmIiIJoGIuIpIAKuYiIgmgYi4ikgAq5nLMMMbMGGPeWvfzJmOMNcZMbmRcIlFQMZdjyeuBq4wxZ1R/fi/uy+LnWpwj0hd0nbkcU4wxLwfeVv26HvedI8/EfY8aB/el11cAh4FbgAncV/J9yVr7dmPMR6s/PxW4wFr7eLdzEGlGj8zlmGKt/TzwA9z3N3919Q24bsV9j/NtwBeBa3GL+D3W2pfgvrvdVXXNfN1a+zwVcuklx/rL+eXY9HFgxFr7SPXn3wQ+aIwByOJ+kMdjwFT1fcSfwH0/+BrbxVhFAtEjcxG3OP9p9ZH5tcAXgFcDeWvtH+O+2+RI9X3hYe27MIr0BD0yF3G3UD5ujElXf34N7htyfdIY8wLcD/T4ITC+QfGJ+NIToCIiCaBtFhGRBFAxFxFJABVzEZEEUDEXEUkAFXMRkQRQMRcRSQAVcxGRBFAxFxFJgP8P0BgUxN1oTGIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "disaster_data = pd.Series([4, 5, 4, 0, 1, 4, 3, 4, 0, 6, 3, 3, 4, 0, 2, 6,\n",
    "                           3, 3, 5, 4, 5, 3, 1, 4, 4, 1, 5, 5, 3, 4, 2, 5,\n",
    "                           2, 2, 3, 4, 2, 1, 3, np.nan, 2, 1, 1, 1, 1, 3, 0, 0,\n",
    "                           1, 0, 1, 1, 0, 0, 3, 1, 0, 3, 2, 2, 0, 1, 1, 1,\n",
    "                           0, 1, 0, 1, 0, 0, 0, 2, 1, 0, 0, 0, 1, 1, 0, 2,\n",
    "                           3, 3, 1, np.nan, 2, 1, 1, 1, 1, 2, 4, 2, 0, 0, 1, 4,\n",
    "                           0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1])\n",
    "years = np.arange(1851, 1962)\n",
    "\n",
    "plt.plot(years, disaster_data, 'o', markersize=8);\n",
    "plt.ylabel(\"Disaster count\")\n",
    "plt.xlabel(\"Year\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Occurrences of disasters in the time series is thought to follow a Poisson process with a large rate parameter in the early part of the time series, and from one with a smaller rate in the later part. We are interested in locating the change point in the series, which perhaps is related to changes in mining safety regulations.\n",
    "\n",
    "In our model, \n",
    "\n",
    "$$ \n",
    "\\begin{aligned}  \n",
    "  D_t &\\sim \\text{Pois}(r_t), r_t= \\begin{cases} \n",
    "   e, & \\text{if } t \\le s \\\\\n",
    "   l, & \\text{if } t \\gt s \n",
    "   \\end{cases} \\\\\n",
    "  s &\\sim \\text{Unif}(t_l, t_h)\\\\         \n",
    "  e &\\sim \\text{exp}(1)\\\\\n",
    "  l &\\sim \\text{exp}(1)    \n",
    "\\end{aligned}\n",
    "$$\n",
    "\n",
    "the parameters are defined as follows: \n",
    "   * $D_t$: The number of disasters in year $t$\n",
    "   * $r_t$: The rate parameter of the Poisson distribution of disasters in year $t$.\n",
    "   * $s$: The year in which the rate parameter changes (the switchpoint).\n",
    "   * $e$: The rate parameter before the switchpoint $s$.\n",
    "   * $l$: The rate parameter after the switchpoint $s$.\n",
    "   * $t_l$, $t_h$: The lower and upper boundaries of year $t$.\n",
    "   \n",
    "This model is built much like our previous models. The major differences are the introduction of discrete variables with the Poisson and discrete-uniform priors and the novel form of the deterministic random variable `rate`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/twiecki/working/projects/pymc/pymc3/model.py:1277: UserWarning: Data in disasters contains missing values and will be automatically imputed from the sampling distribution.\n",
      "  warnings.warn(impute_message, UserWarning)\n"
     ]
    }
   ],
   "source": [
    "with pm.Model() as disaster_model:\n",
    "\n",
    "    switchpoint = pm.DiscreteUniform('switchpoint', lower=years.min(), upper=years.max(), testval=1900)\n",
    "\n",
    "    # Priors for pre- and post-switch rates number of disasters\n",
    "    early_rate = pm.Exponential('early_rate', 1)\n",
    "    late_rate = pm.Exponential('late_rate', 1)\n",
    "\n",
    "    # Allocate appropriate Poisson rates to years before and after current\n",
    "    rate = pm.math.switch(switchpoint >= years, early_rate, late_rate)\n",
    "\n",
    "    disasters = pm.Poisson('disasters', rate, observed=disaster_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The logic for the rate random variable,\n",
    "```python\n",
    "rate = switch(switchpoint >= year, early_rate, late_rate)\n",
    "```\n",
    "is implemented using `switch`, a Theano function that works like an if statement. It uses the first argument to switch between the next two arguments.\n",
    "\n",
    "Missing values are handled transparently by passing a `MaskedArray` or a `pandas.DataFrame` with NaN values to the `observed` argument when creating an observed stochastic random variable. Behind the scenes, another random variable, `disasters.missing_values` is created to model the missing values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unfortunately because they are discrete variables and thus have no meaningful gradient, we cannot use NUTS for sampling `switchpoint` or the missing disaster observations. Instead, we will sample using a `Metroplis` step method, which implements adaptive Metropolis-Hastings, because it is designed to handle discrete values. `PyMC3` automatically assigns the correct sampling algorithms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (2 chains in 2 jobs)\n",
      "CompoundStep\n",
      ">CompoundStep\n",
      ">>Metropolis: [disasters_missing]\n",
      ">>Metropolis: [switchpoint]\n",
      ">NUTS: [late_rate, early_rate]\n",
      "Sampling 2 chains: 100%|██████████| 21000/21000 [00:14<00:00, 1400.63draws/s]\n",
      "/Users/twiecki/anaconda3/lib/python3.6/site-packages/mkl_fft/_numpy_fft.py:1044: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  output = mkl_fft.rfftn_numpy(a, s, axes)\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    }
   ],
   "source": [
    "with disaster_model:\n",
    "    trace = pm.sample(10000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the trace plot below we can see that there's about a 10 year span that's plausible for a significant change in safety, but a 5 year span that contains most of the probability mass. The distribution is jagged because of the jumpy relationship between the year switchpoint and the likelihood  and not due to sampling error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pm.traceplot(trace);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following plot shows the switch point as an orange vertical line, together with its HPD as a semitransparent band. The dashed black line shows the accident rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 8))\n",
    "plt.plot(years, disaster_data, '.')\n",
    "plt.ylabel(\"Number of accidents\", fontsize=16)\n",
    "plt.xlabel(\"Year\", fontsize=16)\n",
    "\n",
    "plt.vlines(trace['switchpoint'].mean(), disaster_data.min(), disaster_data.max(), color='C1')\n",
    "average_disasters = np.zeros_like(disaster_data, dtype='float')\n",
    "for i, year in enumerate(years):\n",
    "    idx = year < trace['switchpoint']\n",
    "    average_disasters[i] = (trace['early_rate'][idx].sum() + trace['late_rate'][~idx].sum()) / (len(trace) * trace.nchains)\n",
    "\n",
    "sp_hpd = pm.hpd(trace['switchpoint'])\n",
    "plt.fill_betweenx(y=[disaster_data.min(), disaster_data.max()],\n",
    "                  x1=sp_hpd[0], x2=sp_hpd[1], alpha=0.5, color='C1');\n",
    "plt.plot(years, average_disasters,  'k--', lw=2);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Arbitrary deterministics\n",
    "\n",
    "Due to its reliance on Theano, PyMC3 provides many mathematical functions and operators for transforming random variables into new random variables. However, the library of functions in Theano is not exhaustive, therefore Theano and PyMC3 provide functionality for creating arbitrary Theano functions in pure Python, and including these functions in PyMC models. This is supported with the `as_op` function decorator.\n",
    "\n",
    "Theano needs to know the types of the inputs and outputs of a function, which are specified for `as_op` by `itypes` for inputs and `otypes` for outputs. The Theano documentation includes [an overview of the available types](http://deeplearning.net/software/theano/library/tensor/basic.html#all-fully-typed-constructors)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import theano.tensor as tt\n",
    "from theano.compile.ops import as_op\n",
    "\n",
    "@as_op(itypes=[tt.lscalar], otypes=[tt.lscalar])\n",
    "def crazy_modulo3(value):\n",
    "    if value > 0: \n",
    "        return value % 3\n",
    "    else :\n",
    "        return (-value + 1) % 3\n",
    "    \n",
    "with pm.Model() as model_deterministic:\n",
    "    a = pm.Poisson('a', 1)\n",
    "    b = crazy_modulo3(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An important drawback of this approach is that it is not possible for `theano` to inspect these functions in order to compute the gradient required for the Hamiltonian-based samplers. Therefore, it is not possible to use the HMC or NUTS samplers for a model that uses such an operator. However, it is possible to add a gradient if we inherit from `theano.Op` instead of using `as_op`. The PyMC example set includes [a more elaborate example of the usage of as_op](https://github.com/pymc-devs/pymc3/blob/master/pymc3/examples/disaster_model_theano_op.py)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Arbitrary distributions\n",
    "\n",
    "Similarly, the library of statistical distributions in PyMC3 is not exhaustive, but PyMC3 allows for the creation of user-defined functions for an arbitrary probability distribution. For simple statistical distributions, the `DensityDist` function takes as an argument any function that calculates a log-probability $log(p(x))$. This function may employ other random variables in its calculation. Here is an example inspired by a blog post by Jake Vanderplas on which priors to use for a linear regression (Vanderplas, 2014). \n",
    "\n",
    "```python\n",
    "import theano.tensor as tt\n",
    "\n",
    "with pm.Model() as model:\n",
    "    alpha = pm.Uniform('intercept', -100, 100)\n",
    "    \n",
    "    # Create custom densities\n",
    "    beta = pm.DensityDist('beta', lambda value: -1.5 * tt.log(1 + value**2), testval=0)\n",
    "    eps = pm.DensityDist('eps', lambda value: -tt.log(tt.abs_(value)), testval=1)\n",
    "    \n",
    "    # Create likelihood\n",
    "    like = pm.Normal('y_est', mu=alpha + beta * X, sigma=eps, observed=Y)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For more complex distributions, one can create a subclass of `Continuous` or `Discrete` and provide the custom `logp` function, as required. This is how the built-in distributions in PyMC are specified. As an example, fields like psychology and astrophysics have complex likelihood functions for a particular process that may require numerical approximation. In these cases, it is impossible to write the function in terms of predefined theano operators and we must use a custom theano operator using `as_op` or inheriting from `theano.Op`. \n",
    "\n",
    "Implementing the `beta` variable above as a `Continuous` subclass is shown below, along with a sub-function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Beta(pm.Continuous):\n",
    "    def __init__(self, mu, *args, **kwargs):\n",
    "        super(Beta, self).__init__(*args, **kwargs)\n",
    "        self.mu = mu\n",
    "        self.mode = mu\n",
    "\n",
    "    def logp(self, value):\n",
    "        mu = self.mu\n",
    "        return beta_logp(value - mu)\n",
    "    \n",
    "\n",
    "def beta_logp(value):\n",
    "    return -1.5 * np.log(1 + (value)**2)\n",
    "\n",
    "\n",
    "with pm.Model() as model:\n",
    "    beta = Beta('slope', mu=0, testval=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If your logp can not be expressed in Theano, you can decorate the function with `as_op` as follows: `@as_op(itypes=[tt.dscalar], otypes=[tt.dscalar])`. Note, that this will create a blackbox Python function that will be much slower and  not provide the gradients necessary for e.g. NUTS."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generalized Linear Models\n",
    "\n",
    "Generalized Linear Models (GLMs) are a class of flexible models that are widely used to estimate regression relationships between a single outcome variable and one or multiple predictors. Because these models are so common, `PyMC3` offers a `glm` submodule that allows flexible creation of various GLMs with an intuitive `R`-like syntax that is implemented via the `patsy` module.\n",
    "\n",
    "The `glm` submodule requires data to be included as a `pandas` `DataFrame`. Hence, for our linear regression example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert X and Y to a pandas DataFrame\n",
    "df = pd.DataFrame({'x1': X1, 'x2': X2, 'y': Y})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model can then be very concisely specified in one line of code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (2 chains in 2 jobs)\n",
      "NUTS: [sd, x2, x1, Intercept]\n",
      "Sampling 2 chains: 100%|██████████| 2000/2000 [00:01<00:00, 1402.82draws/s]\n",
      "/Users/twiecki/anaconda3/lib/python3.6/site-packages/mkl_fft/_numpy_fft.py:1044: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  output = mkl_fft.rfftn_numpy(a, s, axes)\n"
     ]
    }
   ],
   "source": [
    "from pymc3.glm import GLM\n",
    "\n",
    "with pm.Model() as model_glm:\n",
    "    GLM.from_formula('y ~ x1 + x2', df)\n",
    "    trace = pm.sample()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The error distribution, if not specified via the `family` argument, is assumed to be normal. In the case of logistic regression, this can be modified by passing in a `Binomial` family object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pymc3.glm.families import Binomial\n",
    "\n",
    "df_logistic = pd.DataFrame({'x1': X1, 'y': Y > np.median(Y)})\n",
    "\n",
    "with pm.Model() as model_glm_logistic:\n",
    "    GLM.from_formula('y ~ x1', df_logistic, family=Binomial())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For a more complete and flexible formula interface, including hierarchical GLMs, see [Bambi](https://github.com/bambinos/bambi)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Discussion\n",
    "\n",
    "Probabilistic programming is an emerging paradigm in statistical learning, of which Bayesian modeling is an important sub-discipline. The signature characteristics of probabilistic programming--specifying variables as probability distributions and conditioning variables on other variables and on observations--makes it a powerful tool for building models in a variety of settings, and over a range of model complexity. Accompanying the rise of probabilistic programming has been a burst of innovation in fitting methods for Bayesian models that represent notable improvement over existing MCMC methods. Yet, despite this expansion, there are few software packages available that have kept pace with the methodological innovation, and still fewer that allow non-expert users to implement models.\n",
    "\n",
    "PyMC3 provides a probabilistic programming platform for quantitative researchers to implement statistical models flexibly and succinctly. A large library of statistical distributions and several pre-defined fitting algorithms allows users to focus on the scientific problem at hand, rather than the implementation details of Bayesian modeling. The choice of Python as a development language, rather than a domain-specific language, means that PyMC3 users are able to work interactively to build models, introspect model objects, and debug or profile their work, using a dynamic, high-level programming language that is easy to learn. The modular, object-oriented design of PyMC3 means that adding new fitting algorithms or other features is straightforward. In addition, PyMC3 comes with several features not found in most other packages, most notably Hamiltonian-based samplers as well as automatical transforms of constrained random variables which is only offered by Stan. Unlike Stan, however, PyMC3 supports discrete variables as well as non-gradient based sampling algorithms like Metropolis-Hastings and Slice sampling.\n",
    "\n",
    "Development of PyMC3 is an ongoing effort and several features are planned for future versions. Most notably, variational inference techniques are often more efficient than MCMC sampling, at the cost of generalizability. More recently, however, black-box variational inference algorithms have been developed, such as automatic differentiation variational inference (ADVI; Kucukelbir et al., 2017). This algorithm is slated for addition to PyMC3. As an open-source scientific computing toolkit, we encourage researchers developing new fitting algorithms for Bayesian models to provide reference implementations in PyMC3. Since samplers can be written in pure Python code, they can be implemented generally to make them work on arbitrary PyMC3 models, giving authors a larger audience to put their methods into use."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References\n",
    "\n",
    "Patil, A., D. Huard and C.J. Fonnesbeck. (2010) PyMC: Bayesian Stochastic Modelling in Python. Journal of Statistical Software, 35(4), pp. 1-81\n",
    "\n",
    "Bastien, F., Lamblin, P., Pascanu, R., Bergstra, J., Goodfellow, I., Bergeron, A., Bouchard, N., Warde-Farley, D., and Bengio, Y. (2012) “Theano: new features and speed improvements”. NIPS 2012 deep learning workshop.\n",
    "\n",
    "Bergstra, J., Breuleux, O., Bastien, F., Lamblin, P., Pascanu, R., Desjardins, G., Turian, J., Warde-Farley, D., and Bengio, Y. (2010) “Theano: A CPU and GPU Math Expression Compiler”. Proceedings of the Python for Scientific Computing Conference (SciPy) 2010. June 30 - July 3, Austin, TX\n",
    "\n",
    "Lunn, D.J., Thomas, A., Best, N., and Spiegelhalter, D. (2000) WinBUGS -- a Bayesian modelling framework: concepts, structure, and extensibility. Statistics and Computing, 10:325--337.\n",
    "\n",
    "Neal, R.M. Slice sampling. Annals of Statistics. (2003). doi:10.2307/3448413.\n",
    "\n",
    "van Rossum, G. The Python Library Reference Release 2.6.5., (2010). URL http://docs.python.org/library/.\n",
    "\n",
    "Duane, S., Kennedy, A. D., Pendleton, B. J., and Roweth, D. (1987) “Hybrid Monte Carlo”, Physics Letters, vol. 195, pp. 216-222.\n",
    "\n",
    "Stan Development Team. (2014). Stan: A C++ Library for Probability and Sampling, Version 2.5.0.   http://mc-stan.org. \n",
    "\n",
    "Gamerman, D. Markov Chain Monte Carlo: statistical simulation for Bayesian inference. Chapman and Hall, 1997.\n",
    "\n",
    "Hoffman, M. D., & Gelman, A. (2014). The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo. The Journal of Machine Learning Research, 30.\n",
    "\n",
    "Kucukelbir A, Dustin Tran, Ranganath R, Gelman A, and Blei DM. Automatic differentiation variational inference\n",
    " http://arxiv.org/abs/1506.03431,  The Journal of Machine Learning Research. 18 , pp. 430-474 .\n",
    "\n",
    "Vanderplas, Jake. \"Frequentism and Bayesianism IV: How to be a Bayesian in Python.\" Pythonic Perambulations. N.p., 14 Jun 2014. Web. 27 May. 2015. <https://jakevdp.github.io/blog/2014/06/14/frequentism-and-bayesianism-4-bayesian-in-python/>.\n",
    "\n",
    "R.G. Jarrett. A note on the intervals between coal mining disasters. Biometrika, 66:191–193, 1979.\n"
   ]
  }
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